13,720 research outputs found

    A comprehensive survey on cultural algorithms

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    Knowledge Migration Strategies for Optimization of Multi-Population Cultural Algorithm

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    Evolutionary Algorithms (EAs) are meta-heuristic algorithms used for optimization of complex problems. Cultural Algorithm (CA) is one of the EA which incorporates knowledge for optimization. CA with multiple population spaces each incorporating culture and genetic evolution to obtain better solutions are known as Multi-Population Cultural Algorithm (MPCA). MPCA allows to introduce a diversity of knowledge in a dynamic and heterogeneous environment. In an MPCA each population represents a solution space. An individual belonging to a given population could migrate from one population to another for the purpose of introducing new knowledge that influences other individuals in the population. In this thesis, we provide different migration strategies which are inspired from game theory model to improve the quality of solutions. Migration among the different population in MPCA can address the problem of knowledge sharing among population spaces. We have introduced five different migration strategies which are related to the field of economics. The principal idea behind incorporating these strategies is to improve the rate of convergence, increase diversity, better exploration of the search space, to avoid premature convergence and to escape from local optima. Strategies are particularly taken from the economics background as it allows the individual and the population to use their knowledge and make a decision whether to cooperate or to defect with other individuals and populations. We have tested the proposed algorithms against CEC 2015 expensive benchmark problems. These problems are a set of 15 functions which includes varied function categories. Results depict that it leads a to better solution when proposed algorithms used for problems with complex nature and higher dimensions. For 10 dimensional problems the proposed strategies have 7 out 15 better results and for 30 dimensional problems we have 12 out of 15 better results when compared to the existing algorithms

    Evolving Heterogeneous And Subcultured Social Networks For Optimization Problem Solving In Cultural Algorithms

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    Cultural Algorithms are computational models of social evolution based upon principle of Cultural Evolution. A Cultural Algorithm are composed of a Belief Space consisting of a network of active and passive knowledge sources and a Population Space of agents. The agents are connected via a social fabric over which information used in agent problem solving is passed. The knowledge sources in the Belief Space compete with each other in order to influence the decision making of agents in the Population Space. Likewise, the problem solving experiences of agents in the Population Space are sent back to the Belief Space and used to update the knowledge sources there. It is a dual inheritance system in which both the Population and Belief spaces evolve in parallel over generations. A question of interest to those studying the emergence of social systems is the extent to which their organizational structure reflects the structures of the problems that are presented to them. In a recent study [Reynolds, Che, and Ali, 2010] used Cultural Algorithms as a framework in which to empirically address this and related questions. There, a problem generator based upon Langton\u27s model of complexity was used to produce multi-dimensional real-valued problem landscapes of varying complexities. Various homogeneous social networks were then tested against the range of problems to see whether certain homogeneous networks were better at distributing problem solving knowledge from the Belief Space to individuals in the population. The experiments suggested that different network structures worked better in the distribution of knowledge for some optimization problems than others. If this is the case, then in a situation where several different problems are presented to a group, they may wish to utilize more than one network to solve them. In this thesis, we first investigate the advantages of utilizing a heterogeneous network over a suite of different problems. We show that heterogeneous approaches begin to dominate homogeneous ones as the problem complexity increases. A second heterogeneous approach, sub-culutres, will be introduced by dividing the social fabric into smaller networks. The three different social fabrics (homogeneous, heterogeneous and Sub-Cultures) were then compared relative to a variety of benchmark landscapes of varying entropy, from static to chaotic. We show that as the number of independent processes that are involved in the production of a landscape increases, the more advantageous subcultures are in directing the population to a solution. We will support our results with t-test statistics and social fabric metrics performance analysis

    Study the Effects of Multilevel Selection in Multi-Population Cultural Algorithm

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    This is a study on the effects of multilevel selection (MLS) theory in optimizing numerical functions. Based on this theory, a new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new multilevel selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory that states natural selection acts collectively on all the members of a given group. The effects of cooperation are studied using n-player prisoner’s dilemma. In this game, N individuals are randomly divided into m groups and individuals independently choose to be either cooperator or defector. A two-level selection process is introduced namely within group selection and between group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. The principal idea behind incorporating this multilevel selection model is to avoid premature convergence and to escape from local optima and for better exploration of the search space. We test our algorithm using the CEC 2015 expensive benchmark functions to evaluate its performance. These problems are a set of 15 functions which includes varied function categories. We show that our proposed algorithm improves solution accuracy and consistency. For 10 dimensional problems, the proposed method has 8 out 15 better results and for 30-dimensional problems we have 11 out of 15 better results when compared to the existing algorithms. The proposed model can be extended to more than two levels of selection and can also include migration
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