This article presents the design and development of a genetic algorithm to generate long-range transmit codes with low autocorrelation side lobes for radar to minimize target profile estimation error. The GA described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a radar target profile.

Open Shop Scheduling Problem is one of the most important scheduling problems in the field of engineering and industry. This kind of problem includes m machines and n jobs, each job contains a certain number of operations, and each operation has a predetermined processing time on its corresponding machine. The order of processing of these operations affects the completion times of all jobs. Therefore, the purpose of OSSP is to achieve a proper order of processing of jobs using specified machines, (...) so that the completion time of all jobs is minimized. In this paper, the strengths and limitations of three methods are evaluated by comparing the results of solving the OSSP in large-scale and small-scale benchmarks. In this case, the minimized completion time and total tardiness are considered the objective functions of the adapted methods. To solve small-scale problems, we adapt a mathematical model called Multiobjective Mixed Linear Programming. To solve large-scale problems, two metaheuristic algorithms including Multiobjective Parallel Genetic Algorithm and Multiobjective Parallel Simulated Annealing are adapted. In experimental results, we randomly generated small-scale problems to compare MOMILP with the Genetic Algorithm and Simulate Annealing. To compare MOPSA and MOPGA with the state of the art and show their strengths and limitations, we use a standard large-scale benchmark. The simulation results of the proposed algorithms show that although the MOPSA algorithm is faster, the MOPGA algorithm is more efficient in achieving optimal solutions for large-scale problems compared with other methods. (shrink)

This paper presents a GA-based multi-agent reinforce- ment learning bidding approach (GMARLB) for perform- ing multi-agent reinforcement learning. GMARLB inte- grates reinforcement learning, bidding and genetic algo- rithms. The general idea of our multi-agent systems is as follows: There are a number of individual agents in a team, each agent of the team has two modules: Q module and CQ module. Each agent can select actions to be performed at each step, which are done by the Q module. While (...) the CQ module determines at each step whether the agent should continue or relinquish control. Once an agent relinquishes its control, a new agent is selected by bidding algorithms. We applied GA-based GMARLB to the Backgammon game. The experimental results show GMARLB can achieve a su- perior level of performance in game-playing, outperforming PubEval, while the system uses zero built-in knowledge. (shrink)

This paper is about representations for Artificial Intelligence systems. All of the results described in it involve engineering the representation to make AI systems more effective. The main AI techniques studied here are varieties of search: path-finding in graphs, and probablilistic searching via simulated annealing and genetic algorithms. The main results are empirical findings about the granularity of representation in implementations of genetic algorithms. We conclude by proposing a new algorithm, called “Long-Term Evolution,” which is a (...) genetic algorithm running on an evolving problem description. We see this as modelling the evolution of a species from simpler (more coarsely described— fewer genes) types of organisms to more complex ones. The results, which are reported here of our experiments with the algorithm make it seem a promising optimisation technique. (shrink)

Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive (...) assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools and methods, in response to the needs of the student information management system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions. (shrink)

The estrangement between genetic scientists and theologians originating in the 1960s is reflected in novel combinations of human thought (subject) and genes (investigational object), paralleling each other through the universal process known in chaos theory as self-similarity. The clash and recombination of genes and knowledge captures what Philip Hefner refers to as irony, one of four voices he suggests transmit the knowledge and arguments of the religion-and-science debate. When viewed along a tangent connecting irony to leadership, journal (...) dissemination, and the activities of the "public intellectual" and the public at large, the sequence of voices is shown to resemble the passage of genetic information from DNA to mRNA, tRNA, and protein, and from cell nucleus to surrounding environment. In this light, Hefner's inquiry into the voices of Zygon is bound up with the very subject matter Zygon covers. (shrink)

Novel technological devices, applications, and algorithms can provide us with a vast amount of personal information about ourselves. Given that we have ethical and practical reasons to pursue self-knowledge, should we use technology to increase our self-knowledge? And which ethical issues arise from the pursuit of technologically sourced self-knowledge? In this paper, I explore these questions in relation to bioinformation technologies (health and activity trackers, DTC genetic testing, and DTC neurotechnologies) and algorithmic profiling used for (...) recommender systems, targeted advertising, and technologically supported decision-making. First, I distinguish between impersonal, critical, and relational self-knowledge. Relational self-knowledge is a so far neglected dimension of self-knowledge which is introduced in this paper. Next, I investigate the contribution of these technologies to the three types of self-knowledge and uncover the connected ethical concerns. Technology can provide a lot of impersonal self-knowledge, but we should focus on the quality of the information which tends to be particularly insufficient for marginalized groups. In terms of critical self-knowledge, the nature of technologically sourced personal information typically impedes critical engagement. The value of relational self-knowledge speaks in favour of transparency of information technology, notably for algorithms that are involved in decision-making about individuals. Moreover, bioinformation technologies and digital profiling shape the concepts and norms that define us. We should ensure they not only serve commercial interests but our identity and self-knowledge interests. (shrink)

Estimation of Distribution Algorithms, which employ probabilistic models to generate the next population, are new promising methods in the field of genetic and evolutionary algorithms. In the case of conventional Genetic and Evolutionary Algorithms are applied to Constraint Satisfaction Problems, it is well-known that the incorporation of the domain knowledge in the Constraint Satisfaction Problems is quite effective. In this paper, we constitute a memetic algorithm as a combination of the Estimation of Distribution Algorithm (...) and a repair method. Experimental results on general Constraint Satisfaction Problems tell us the effectiveness of the proposed method. (shrink)

This paper begins with the thought that there is something out of place about offloading inquiry into one's own mind to AI. The paper's primary goal is to articulate the unease felt when considering cases of doing so. It draws a parallel between the use of algorithms in the criminal law: in both cases one feels entitled to be treated as an exception to a verdict made on the basis of a certain kind of evidence. Then it identifies (...) an account of first‐person authority that can make good on this: agentialism. Thus, the paper constitutes an argument in favor of an agentialist treatment of self‐knowledge and first‐person authority. (shrink)

Parallel processing as a method to improve computer performance has become a development trend. Based on rough set theory and divide-and-conquer idea of knowledge reduction, this paper proposes a classification method that supports parallel attribute reduction processing, the method makes the relative positive domain which needs to be calculated repeatedly independent, and the independent relative positive domain calculation could be processed in parallel; thus, attribute reduction could be handled in parallel based on this classification method. (...) Finally, the proposed algorithm and the traditional algorithm are analyzed and compared by experiments, and the results show that the proposed method in this paper has more advantages in time efficiency, which proves that the method could improve the processing efficiency of attribute reduction and makes it more suitable for massive data sets. (shrink)

Member selection to form an effective collaboration new product development team is crucial for a successful NPD. Existing researches on member selection mostly focus on the individual attributes of candidates. However, under the background of collaboration, knowledge complementarity and collaboration performance among candidates are important but overlooked. In this paper, we propose a multi-objective optimization model for member selection of a Co-NPD team, considering comprehensively the individual knowledge competence, knowledge complementarity, and collaboration performance. Then, to solve the (...) model, an improved adaptive genetic algorithm is developed. Finally, a real case is provided to illustrate the application of the model, and the IAGA is implemented to select the desired team members for optimal team composition. Additionally, the standard generic algorithm and particle swarm optimization are used to compare with the IAGA to further verify the effectiveness of the IAGA. (shrink)

Modern society makes extensive use of automated algorithmic decisions, fueled by advances in artificial intelligence. However, since these systems are not perfect, questions about fairness are increasingly investigated in the literature. In particular, many authors take a Rawlsian approach to algorithmic fairness. This article aims to identify some complications with this approach: Under which circumstances can Rawls’s original position reasonably be applied to algorithmic fairness decisions? First, it is argued that there are important differences between Rawls’s original position and a (...) parallel algorithmic fairness original position with respect to risk attitudes. Second, it is argued that the application of Rawls’s original position to algorithmic fairness faces a boundary problem in defining relevant stakeholders. Third, it is observed that the definition of the least advantaged, necessary for applying the difference principle, requires some attention in the context of algorithmic fairness. Finally, it is argued that appropriate deliberation in algorithmic fairness contexts often require more knowledge about probabilities than the Rawlsian original position allows. Provided that these complications are duly considered, the thought-experiment of the Rawlsian original position can be useful in algorithmic fairness decisions. (shrink)

A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show that emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of "communicators" develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. (...) Here we focus on environmental variability, comparing results for environments with (a) constant resources, (b) random resources, and (c) cycles of "boom and bust." In both simple and complex models across all three mechanisms of strategy change, the emergence of communication is strongly favored by cycles of "boom and bust." These results are particularly intriguing given the importance of environmental variability in fields as diverse as psychology, ecology and cultural anthropology. (shrink)

Crew Pairing is one of the most important and difficult problems for airline companies. Nets to fuel costs, the crew costs constitute the largest cost of airlines, and the crew costs depend on the quality of the solution to the pairing problem. Conventional systems have been used to solve a daily model, which handles only regular flights with many simplifications, so a lot of corrections are needed to get a feasible solution and the quality of the solution is not so (...) high. A fully dated model, which handles regular flights and irregular flights simultaneously, is extremely hard and has not been solved directly hitherto. The number of irregular flights tend to increase in Europe and Japan, hence the resolution of the practical fully dated pairing system is desired. This paper presents a new approach which solves directly the fully dated crew pairing by Genetic Algorithms. GA is improved with stochastic processes to attack the fully dated model, and many heuristics are included in the decoding mecanism of the GA. For several hundred flights per day for fully dated 2 months scheduling period, the system found better or equivalent solutions to the human scheduler’s without any simplification from 5 to 20 times faster. (shrink)

In representing classification rules by decision trees, simplicity of tree structure is as important as predictive accuracy especially in consideration of the comprehensibility to a human, the memory capacity and the time required to classify. Trees tend to be complex when they get high accuracy. This paper proposes a novel method for generating accurate and simple decision trees based on symbiotic evolution. It is distinctive of symbiotic evolution that two different populations are evolved in parallel through genetic (...) class='Hi'>algorithms. In our method one's individuals are partial trees of height 1, and the other's individuals are whole trees represented by the combinations of the former individuals. Generally, overfitting to training examples prevents getting high predictive accuracy. In order to circumvent this difficulty, individuals are evaluated with not only the accuracy in training examples but also the correct answer biased rate indicating the dispersion of the correct answers in the terminal nodes. Based on our method we developed a system called SESAT for generating decision trees. Our experimental results show that SESAT compares favorably with other systems on several datasets in the UCI repository. SESAT has the ability to generate more simple trees than C5.0 without sacrificing predictive accuracy. (shrink)

Evolutionary computation has been applied to numerous design tasks, including design of electric circuits, neural networks, and genetic circuits. Though it is a very effective solution for optimizing network structures, genetic algorithm faces many difficulties, often referred to as the permutation problems, when both topologies and the weights of the network are the target of optimization. We propose a new crossover method used in conjunction with a genotype with information tags. The information tags allow GA to recognize and (...) preserve the common structure of parent chromosomes during genetic crossover. The method is implemented along with subpopulating strategies to make the parallel evolution of network topology and weights feasible and efficient. The proposed method is evaluated on a few typical and practical problems, and shows improvement from conventional methodologies and genotypes. (shrink)

Schedule planning is one of the most crucial issues for any airline company, because the profit of the company directly depends on the efficiency of the schedule. This paper presents a novel scheduling method which solves problems related to time scheduling, fleet assignment and maintenance routing simultaneously by Genetic Algorithms. Every schedule constraint is embeded in the fitness function, which is described as an object oriented model and works as a simulater developing itself over time, and whose solution (...) is executable without human correction. The schedular is able to solve the problems involving several hundred flights in a few hours, and the solutions are superior or equivalent to those by human experts in terms of the estimated profit. (shrink)

Probabilistic predictions of future risk govern much of society: healthcare, genetics, social media, national security, and finance. Both policy-makers and private companies are increasingly working to design institutional structures that seek to manage risk by controlling the behavior of citizens and consumers, using new technologies of predictive control that comb through past data to predict and shape future action. These predictions not only control social institutions but also shape individual character and forms of practical reason. Risk-based decision theory shifts people's (...) relationships to the future through knowledge of possible dangers and foregone opportunities, thus inspiring anxious solicitude and deceptive hopes for total security. This book uses virtue ethics to analyze these problems and to suggest ways to enjoy these technologies' positive benefits while constraining their dangerous aspects through a better understanding of practical reasoning and a theological analysis of our responsibility for future risks. (shrink)

Partner selection is the primary aspect of the formation of knowledge collaboration teams. We propose a method of partner selection for KCTs based on a weighted social network analysis method in which the individual knowledge competence and the collaboration performance of candidates are both considered. To select the desired partners, a biobjective 0-1 model is built, integrating the knowledge competence and collaboration performance, which is an NP-hard problem. Then, a multiobjective genetic algorithm is developed to solve (...) the proposed model. Finally, a real-world example is provided to illustrate the applicability of the model, and the MOGA is implemented to search for Pareto solutions of partner selection for KCT in this case. Moreover, some simulation examples are used to test the efficiency of the algorithm. The results suggest that the proposed method can support effective and practical partner selection. (shrink)

The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create automated systems with similar or better performance. For instance, NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions. Spectrometer readings provide one important source of data for identifying sites with minerals of interest. Reflectance spectrometers measure intensities (...) of light reflected from surfaces over a range of wavelengths. Spectral intensity patterns may in some cases be sufficiently distinctive for proper identification of minerals or classes of minerals. For some mineral classes, carbonates for example, specific short spectral intervals are known to carry a distinctive signature. Finding similar distinctive spectral ranges for other mineral classes is not an easy problem. We propose and evaluate data-driven techniques in two stages: first, evaluating algorithms to identify which components are probably present in a given rock; second, trying to improve this classification by automatically searching for spectral ranges optimized for specific classes of minerals. In one set of studies, we partition the whole interval of wavelengths available in our data into sub-intervals, or bins, and use a genetic algorithm to evaluate a candidate selection of subintervals. As an alternative to these computationally expensive search techniques, we present an entropy-based heuristic that gives higher scores for wavelengths more likely to distinguish between classes. Results are presented for four different classes, showing reasonable improvements in identifying some, but not all, of the mineral classes tested. (shrink)

Shane Harwood and Richard Scheines. Genetic Algorithm Search Over Causal Models.

This paper argues for the possibility of 'artificial life' and computational evolution, first by discussing (via a highly simplified version) John von Neumann's self-reproducing automaton and then by presenting some recent work focusing on computational evolution, in which 'cellular automata', a form of parallel and decentralized computing system, are evolved via 'genetic algorithms'. It is argued that such in silico experiments can help to make sense of the question of whether we can eventually build computers that are (...) intelligent and alive. (shrink)

Reinforcement learning is very effective for robot learning. Because it does not need priori knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforcement learning algorithm: “Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)”. It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. And we applied it to 50 link manipulator and effective behavior is acquired. However (...) optimality and fault tolerance of the proposed algorithm were not considered and to demonstrate effectiveness of the proposed algorithm other applications are necessary. Acquiring of locomotion patterns by a multi-legged robot is a very interesting problem. As it has many redundant degrees of freedom, application of usual reinforcement learning is difficult and an optimal locomotion has not been acquired using previous reinforcement learning algorithm. And the redundancy of the robot is effective to the fault tolerance and various locomotion patterns can be acquired for adapting the faults of the legs. In this paper, we applied QDSEGA to acquiring of locomotion pattern by the multi-legged robot and considered the optimality and fault tolerance. Effective behavior has been obtained by using our proposed algorithm. (shrink)

This study aims to develop a hybrid model to represent the human mind from a functionalist point of view that can be adapted to artificial intelligence. The model is not a realistic theory of the neural network of the brain but an instrumentalist AI model, which means that there can be some other representative models too. It had been thought that the provability of an axiomatic system requires the completeness of a formal system. However, Gödel proved that no consistent formal (...) system can prove its own consistency. There is a paradoxical limit to provability. Both consistency and completeness are impossible together. These formal limits form the basis of our model: the hybrid model of the mind consists of a formal axiomatic system and an evolutionary algorithm (EA) of intelligence, which also includes the genetic algorithm (GA) of consciousness. The GA of consciousness operates based on intentions, functioning in conjunction with the principles of EA. This collaboration allows it to transcend the paradoxical formal limits of intelligence. However, GA overcomes the problem of paradoxicality at the cost of producing illusions. Following this collaboration gives us a GA operating system. After all, if the rational optimization task of the GA in question produces illusions, then the rationality must lie elsewhere in the paradox. (shrink)

The travelling salesman problem (TSP) belongs to the class of NP-hard problems, in which an optimal solution to the problem cannot be obtained within a reasonable computational time for large-sized problems. To address TSP, we propose a hybrid algorithm, called GA-TCTIA-LBSA, in which a genetic algorithm (GA), tour construction and tour improvement algorithms (TCTIAs) and a list-based simulated annealing (LBSA) algorithm are used. The TCTIAs are introduced to generate a first population, and after that, a search is continued (...) with the GA. The problem of premature convergence of the GA to local optimum is tackled by a method called social disaster technique. Afterwards, the LBSA is applied to generate a new population based on one of two proposed operators called packing and judgement day. The proposed algorithm is implemented in the MATLAB environment, and its two variants, called GA-TCTIA-LBSA packing and GA-TCTIA-LBSA judgement day, are tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed GA-TCTIA-LBSAs offer promising results, particularly for small-sized instances. (shrink)

The Vehicle Routing Problem (VRP) has been extensively studied by different researchers from all over the world in recent years. Multiple solutions have been proposed for different variations of the problem, such as Capacitive Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Time Windows (VRP-TW), Vehicle Routing Problem with Pickup and Delivery (VRPPD), among others, all of them with deterministic times. In the last years, researchers have been interested in including in their different models the variations that travel times may (...) experience when exposed to all kind of phenomena, mainly vehicle traffic. This article addresses the VRP from this perspective, proposing the design and implementation of a genetic algorithm based on neutrosophic theory for calculating the fitness function of each route, considering the variability and uncertainty present in travel times. A deterministic genetic algorithm is also implemented with the average travel times to compare it with the neutrosophic algorithm using simulation. As conclusion, a deterministic algorithm does not necessarily generate the best solution in the real world, full of uncertainty. Also, the quantification of uncertainty using neutrosophic theory can be used in route planning, opening a broad and interesting field of research for future investigations. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Descartes and Husserl. The Philosophical Project of Radical BeginningsMichael K. ShimPaul S. MacDonald. Descartes and Husserl. The Philosophical Project of Radical Beginnings. Albany: State University of New York Press, 2000. Pp. 285. Paper, $21.95.The enormous influence exerted by Descartes on Husserl's phenomenological philosophy cannot be underestimated. Not only is Husserl quite open and explicit about his philosophical debt to Descartes, but the fundamental motivation of the phenomenological [End (...) Page 593] method (directed towards apodeicticity and the purification of consciousness) would most likely be meaningless without reference to the Cartesian precedent. Consequently, almost every exegetical approach to Husserlian phenomenology has at least mentioned the relevance of the Cartesian tradition. Unfortunately, rare has been the phenomenological treatment of Descartes current with the latest scholarship. As an exception to this general shortcoming, MacDonald's book is a most welcome entry into Husserl scholarship.One of MacDonald's main theses is that Descartes and Husserl shared similarities in historical circumstances and, thus, shared similar motives for their "radical beginnings" in philosophy. With regards to Descartes' historical circumstances, by providing a breathtaking overview of sixteenth-century skepticism, MacDonald gives a cogent account of what led to Descartes' attempted construction of an absolute epistemological foundation for philosophy. However, the "conceptual confusions and collapses" of neo-skepticism will "survive the Cartesian overthrow and, like a persistent contagion" (31) reappear in the form of nineteenth-century psychologism. MacDonald does a convincing job of highlighting the skeptical profile of psychologism by providing systematic parallels between the mainstays of neo-skepticism and nineteenth-century psychologism: namely, distrust of objective knowledge, subjective reductionism and epistemological relativism. Accordingly, what motivates Husserl's epoché has both historical and systematic resources in the Cartesian reduction. The point itself is nothing new, and Husserl himself stresses the skepticism of nineteenth-century psychologism, but the scholarship and erudition that inform MacDonald's detailed historical account is to be commended.The truly ambitious aspect of the book, however, is the painstakingly worked out systematic comparison between the respective philosophical projects. In this respect, MacDonald takes two correlated approaches. The first approach takes up the metaphors of discovery and "voyage of exploration" (5) common to both thinkers' writings in order to explicate the meaning of philosophical transformation or "conversion," a "cognitive-affective reorientation of the whole self" (227). However, these metaphors are to be understood as symptomatic of deeper formal parallels between the two thinkers.The second approach begins with the daring claim that "Descartes' and Husserl's formal ontologies are... functionally equivalent" (95), a claim MacDonald proposes to reinforce by examining the structural parallels between Descartes' simple-complex dyad on the one hand and Husserl's part-whole theory as introduced in the Third Logical Investigations. This claim is brought into relief by a reformation of Descartes' conception of "ideas." Rather than restrict the Cartesian conception of ideas to "objective content," MacDonald emphasizes the act-quality of Cartesian ideas (125-7). Thereby an intentional conception of Cartesian ideas is made possible. Anyone familiar with Husserl should not find this surprising: what MacDonald is trying to suggest is a division of Cartesian ideas into noesis and noema, with the "material" aspect of act-ideas finding phenomenological restoration in Husserl's "hyletic data." With these elements in place, MacDonald largely succeeds in constructing a fuller and richer than usual comparison between methodical doubt and the phenomenological reduction (151ff), centered on a non-mystical eidetic intuitionism. MacDonald's ultimate conclusion is [End Page 594] that Husserlian phenomenology, as "first philosophy," is thoroughly consistent with Cartesian metaphysics, now understood as "second philosophy" (243).MacDonald's systematic conclusions are much bolder than one might think, since they are adduced by running against the letter of Husserl's own interpretation of Descartes. The background claim is that Husserl himself did not fully appreciate his debt to Descartes, and it is this exegetical failure that has led to his eventual abandonment of the "Cartesian Way." However, in order to make such an assertion, MacDonald would be obligated to demonstrate the Cartesian influences on the phenomenological pillars of the later Husserl: i.e., temporalization, genetic analysis and the theory of intersubjectivity—as programmatically outlined in the Fourth and... (shrink)

Reinforcement learning has recently received much attention as a learning method for complicated systems, e.g., robot systems. It does not need prior knowledge and has higher capability of reactive and adaptive behaviors. However increase in dimensionality of the action-state space makes it diffcult to accomplish learning. The applicability of the existing reinforcement learning algorithms are effective for simple tasks with relatively small action-state space. In this paper, we propose a new reinforcement learning algorithm: “Q-learning with Dynamic Structuring of (...) Exploration Space Based on Genetic Algorithm ”. The algorithm is applicable to systems with high dimensional action and interior state spaces, for example a robot with many redundant degrees of freedom. To demonstrate the effectiveness of the proposed algorithm simulations of obstacle avoidance by a 50 links manipulator have been carried out. It is shown that effective behavior can be learned by using the proposed algorithm. (shrink)

The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create automated systems with similar or better performance. For instance, NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions. Spectrometer readings provide one important source of data for identifying sites with minerals of interest. Reflectance spectrometers measure intensities (...) of light reflected from surfaces over a range of wavelengths. Spectral intensity patterns may in some cases be sufficiently distinctive for proper identification of minerals or classes of minerals. For some mineral classes, carbonates for example, specific short spectral intervals are known to carry a distinctive signature. Finding similar distinctive spectral ranges for other mineral classes is not an easy problem. We propose and evaluate data-driven techniques in two stages: first, evaluating algorithms to identify which components are probably present in a given rock; second, trying to improve this classification by automatically searching for spectral ranges optimized for specific classes of minerals. In one set of studies, we partition the whole interval of wavelengths available in our data into sub-intervals, or bins, and use a genetic algorithm to evaluate a candidate selection of subintervals. As an alternative to these computationally expensive search techniques, we present an entropy-based heuristic that gives higher scores for wavelengths more likely to distinguish between classes. Results are presented for four different classes, showing reasonable improvements in identifying some, but not all, of the mineral classes tested. (shrink)

The need to regulate access to genetic resources and ensure a fair and equitable sharing of any resulting benefits was at the core of the development of the Convention on Biological Diversity (CBD). The CBD established a series of principles and requirements around access and benefit sharing (ABS) in order to increase transparency and equity in the international flow of genetic resources, yet few countries have been able to effectively implement them and ABS negotiations are often paralysed by (...) differing interests. -/- This book not only examines these complex challenges, but offers workable, policy-oriented solutions. International contributors cover theoretical approaches, new significant national legislation, the concept of traditional knowledge, provider and user country measures and common solutions. Exploring specific, salient examples from across the globe, the authors provide lessons for national regulation and the ongoing negotiations for an international ABS regime. Uniquely, this book also looks at the potential for 'horizontal' development of ABS law and policy, applying lessons from bilateral approaches to other national contexts. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm (...) and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

The paper discusses the problem of assignment production resources in executing a production order on the example of the car rims manufacturing process. The more resources are involved in implementing the manufacturing process and the more they can be used interchangeably, the more complex and problematic the scheduling process becomes. Special attention is paid to the effective scheduling and assignment of rim machining operations to production stations in the considered manufacturing process. In this case, the use of traditional scheduling methods (...) based on simple calculations, or the know-how of process engineers often turns out to be insufficient to achieve the intended results. Due to the scale of the problems faced in practice, the methods based on approximate approaches (Genetic and Tabu Search) were used to solve them. In this perspective, the problem under consideration involves the extension of the classic assignment problem with the possibility of taking into account: the times of operations, potential changeovers and the capacity of production resources. (shrink)

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I (...) propose to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity. (shrink)

As the first stage of the formation of a collaborative new product innovation team, member selection is crucial for the effective operation of the CNPI team and the achievement of new product innovation goals. Considering comprehensively the individual and collaborative attributions, the individual knowledge competence, knowledge complementarity, and collaborative performance among candidates are chosen as the criteria to select CNPI team members in this paper. Moreover, using the fuzzy set and social network analysis method, the quantitative methods of (...) the above criteria are proposed correspondingly. Then, by integrating the above criteria, a novel multiobjective decision model for member selection of the CNPI team is built from the view of individual and collaborative attributions. Since the proposed model is NP-hard, a double-population adaptive genetic algorithm is further developed to solve it. Finally, a real case is provided to illustrate the application and effectiveness of the proposed model and method in this paper. (shrink)

The well-known classifier support vector machine has many parameters associated with its various kernel functions. The radial basis function kernel, being the most preferred kernel, has two parameters to be optimized. The problem of optimizing these parameter values is called model selection in the literature, and its results strongly influence the performance of the classifier. Another factor that affects the classification performance of a classifier is the feature subset. Both these factors are interdependent and must be dealt with simultaneously. Following (...) the multiobjective definition of feature selection, we have applied a multiobjective genetic algorithm, NSGA II, to optimize the feature subset and model parameters simultaneously. Comparison of the proposed approach with the grid algorithm and GA-based method suggests that the MOGA-based approach performs better than the grid algorithm and is as good as the GA-based approach. Moreover, it provides multiple solutions instead of a single solution. The users can prefer one feature subset over the other as per their requirement and available resources. (shrink)

The deep formal and conceptual link existing between artificial life and artificial intelligence can be highlighted using conceptual tools derived by Karl Popper's evolutionary epistemology.Starting from the observation that the structure itself of an organism embodies knowledge about the environment which it is adapted to, it is possible to regard evolution as a learning process. This process is subject to the same rules indicated by Popper for the growth of scientific knowledge: causal conjectures (mutations) and successive refutations (extinction). (...) In the field of machine learning such a paradigm is represented by genetic algorithms that, simulating biological processes, emulate cognitive processes. From a practical viewpoint, that perspective allows to identify the two different kinds of learning considered by artificial intelligence, knowledge acquisition and skill improvement, and to get a different view of the problem of heuristic knowledge in learning systems. From a theoretical point of view, these considerations can shade a new light on an old epistemological problem: why do we live in a learnable world? (shrink)

ABSTRACT Scientific advances in genetic analysis have been made possible in recent years by technical developments in computational biology, or bioinformatics. Bioinformatics has opened up the human genome to diverse analyses involving automated laboratory hardware and machine learning algorithms and software. As part of an emerging field of social genomics, recent educational genetics studies using big data have begun to raise challenging findings linking DNA to predicted life outcomes. Bioinformatic technologies and techniques including ‘genome-wide association’ and ‘polygenic scoring’ (...) are producing new kinds of genetic biodata and expert knowledge for rethinking the upbringing and education of children. This article takes as an empirical opening a recent epistemic and ethical controversy over the use of biodata to make genetic predictions about educational, socio-economic and life outcomes, detailing the forms of expert knowledge and technologies involved these predictions, and exploring the social and ethical implications of data-intensive bioinformatics. (shrink)

Since the opening of the economy, China’s regional economy has developed rapidly, the overall national strength has been increasing, and the people’s living standards have been continuously improved. The issue of coordinated regional development has become an important issue in today’s society. Genetic algorithm is a kind of prediction algorithm that has developed rapidly in recent years and is widely used. However, when solving engineering prediction problems, there are often problems such as premature convergence and easiness to fall into (...) local optimal solutions. Therefore, on the basis of studying the related theories of genetic algorithm and artificial immune algorithm, this paper uses the advantages of the two algorithms, combines the two algorithms, and proposes an improved algorithm for genetic algorithm-adaptive immune genetic algorithm. Taking genetic algorithm as the basic framework, the operators and selection methods of artificial immune algorithm are integrated. Using the adaptive concept, the formulas of adaptive crossover probability and mutation probability are innovatively designed. Compared with the fixed value of the immune genetic algorithm, the introduction of the adaptive concept can intelligently adjust the optimization process and increase the optimization speed. Considering the double uncertain factors of product market demand and waste product recycling in the remanufacturing supply chain system, the maximization of logistics network operating profit, the minimization of environmental impact, and the maximization of customer satisfaction are the forecast goals. The market demand of uncertain products is effectively controlled through the option contract mechanism, and a multiobjective forecasting model based on the option contract mechanism is established. According to the characteristics of the model, an improved immune genetic algorithm is designed to solve the problem, and the effectiveness of the immune genetic algorithm is verified through an example. (shrink)