niching genetic algorithms
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Niching Genetic Algorithms. Motivation The Idea Ecological Meaning Niching Techniques. Niching Genetic Algorithms: Motivation. Multimodal Optimization - PowerPoint PPT PresentationTRANSCRIPT
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Niching Genetic Algorithms
• Motivation• The Idea• Ecological Meaning• Niching Techniques
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Niching Genetic Algorithms: Motivation• Multimodal Optimization• Traditional genetic algorithms with elitist selection are s
uitable to locate the optimum of unimodal functions as they converge to a single solution of the search space.
• Real problem, however, often require the identification of optima along with some local optima.
• For this purpose, niching methods extend the simple genetic algorithms by promoting the formation of subpopulations in the neighborhood of the local optimal solutions.
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Canonical GA
Niching GA
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Niching Genetic Algorithms: The Idea• Niching methods have been developed to reduce t
he effect of genetic drift resulting from the selection operator in the simple genetic algorithms.
• They maintain population diversity and permit genetic algorithms to explore more search space so as to identify multiple peaks, whether optimal or otherwise.
• The fitness sharing method is probably the best known and best used among the niching techniques.
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Ecological Meaning (1)
• In natural ecosystem, a niche can be viewed as an organisms task, which permits species to survive in their environment.
• Species are defined as a collection of similar organisms with similar features.
• The subdivision of environment on the basis of an organisms role reduces inter-species competition for environmental resources.
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Ecological Meaning (2)
• This reduction in competition helps stable sub-populations to form around different niches in the environment.
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Analogy
• By analogy, in multimodal GAs, a niche is commonly referred to as the location of each optimum in the search space and the fitness representing the resource of that niche.
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Genetic Niching: Niching Techniques
• The Fitness Sharing Method• Crowding Method• Clearing Method
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Genetic Niching: The Sharing Method
• Literature• Essence
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The Fitness Sharing Method: Literature
• Holland, J. H. (1975), Adaptation in Natural and Artificial Systems, MIT Press.
• Goldberg, D. E. and J. Richardson (1987), “Genetic Algorithms with Sharing for Multimodal Function Optimization,” in J. Grefensette (ed.), Proceedings of the 2nd International Conference on Genetic Algorithms, Lawrence Erlbaum. pp. 41-49.
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The Fitness Sharing Method: Essence
• The sharing method essentially modifies the search landscape by reducing the payoff in densely populated regions.
• This method rewards individuals that uniquely exploit areas of the domain, while discouraging highly similar individuals in a domain.
• This causes population diversity pressure, which helps maintain population members at local optima.
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Genetic Niching: The Crowding Method
• Standard Crowding Method (DeJong, 1975):• Deterministic Crowing Method (Mahfoud, 1995):
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Genetic Niching: Standard Crowding Method• Standard crowding method was proposed by DeJong (1
975).• In this method, only a fraction of the global population s
pecified by a percentage G (generation gap) reproduces and dies in each generation.
• An offspring replaces the most similar individual (in terms of genotype comparison) taken from a randomly drawn subpopulation of size CF (crowding factor) from the global population.
• This method has been found to be limited in multimodal function optimization.
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Genetic Niching: Deterministic Crowing Method
• Mahfoud (1995) improved DeJong's standard crowding and propose his deterministic crowding method.
• Deterministic crowding method introduces competition between children and parents of identical niches.
• This method tends to identify solutions with higher fitness and lose solutions with lower fitness.
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Genetic Niching: Clearing Method
• Clearing method was proposed by Petrowski (1996, 1997) based on limited resources of environment.
• Instead of evenly sharing the available resources among the individuals of a subpopulation, the clearing procedure supplies these resources only to the best individuals of each subpopulation.
• Clearing method is found to be most suitable to deal with the extremely complicated search space of the portfolio optimization problem.
• Technical Details
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Clearing Method: Technical Details
• Notations• The Three Functions• Algorithm
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Clearing Method: Some Notations
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1
2kappa
Sigma
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Clearing Method: The Three Functions
• The clearing procedure uses three functions:
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Clearing Method: The Algorithm
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Clearing Method: Description