Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Punctuated equilibria: a parallel genetic algorithm
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Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Co-evolving parasites improve simulated evolution as an optimization procedure
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GENITOR II.: a distributed genetic algorithm
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Biologically influenced algorithms and parallelism in non-linear optimization
Biologically influenced algorithms and parallelism in non-linear optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Classifier Systems and the Animat Problem
Machine Learning
Learning with Genetic Algorithms: An Overview
Machine Learning
Improving the Performance of Genetic Algorithms in Classifier Systems
Proceedings of the 1st International Conference on Genetic Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
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Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
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IEEE Transactions on Fuzzy Systems
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Artificial Life
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ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
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PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees
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IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
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In typical applications, genetic algorithms (GAs) process populations of potential problem solutions to evolve a single population member that specifies an 'optimized' solution. The majority of GA analysis has focused on these optimization applications. In other applications (notably learning classifier systems and certain connectionist learning systems), a GA searches for a population of cooperative structures that jointly perform a computational task. This paper presents an analysis of this type of GA problem. The analysis considers a simplified genetics-based machine learning system: a model of an immune system. In this model, a GA must discover a set of pattern-matching antibodies that effectively match a set of antigen patterns. Analysis shows how a GA can automatically evolve and sustain a diverse, cooperative population. The cooperation emerges as a natural part of the antigen-antibody matching procedure. This emergent effect is shown to be similar to fitness sharing, an explicit technique for multimodal GA optimization. Further analysis shows how the GA population can adapt to express various degrees of generalization. The results show how GAs can automatically and simultaneously discover effective groups of cooperative computational structures.