Evolutionary Computation (ec) eie426-eC-200809. ppt 2021/1/30 EIE426-AICV
2021/1/30 EIE426-AICV 1 Evolutionary Computation (EC) eie426-ec-200809.ppt
Contents Basic concepts of ec Genetic Algorithms An Example Chromosome Representation Stopping Criteria Initial Population Selection Mechanisms Crossover and mutation Fⅰ itness Functions Another Example Application Routing Optimization Advantages and disadvantages of ec 2021/1/30 EIE426-AICV
2021/1/30 EIE426-AICV 2 Contents ◼ Basic Concepts of EC ◼ Genetic Algorithms ◼ An Example ◼ Chromosome Representation ◼ Stopping Criteria ◼ Initial Population ◼ Selection Mechanisms ◼ Crossover and Mutation ◼ Fitness Functions ◼ Another Example ◼ Application: Routing Optimization ◼ Advantages and Disadvantages of EC
Evolution and search Evolution-search through the enormous genetic parameter space for the best genetic make-up Borrow ideas from nature to help us solve problems that have equally large search spaces or similarly changing environment 2021/1/30 EIE426-AICV
2021/1/30 EIE426-AICV 3 Evolution and Search ◼ Evolution - search through the enormous genetic parameter space for the best genetic make-up. ◼ Borrow ideas from nature to help us solve problems that have equally large search spaces or similarly changing environment
Natural Evolution and Evolutionary Computation Natural Evolutionary Evolution Computing Individual Candidate Solution Fitness Quality Environment Problem 2021/1/30 EIE426-AICV
2021/1/30 EIE426-AICV 4 Natural Evolution and Evolutionary Computation Natural Evolution Individual Fitness Environment Evolutionary Computing Candidate Solution Quality Problem
Different ecs Several classes of EC algorithms have been developed Genetic algorithms(GAs): model genetic evolution Genetic programming: based on GAs, but individuals are programs (represented as trees Evolutionary programming: from the simulation of adaptive behavior in evolution(phenotype evolution Evolution strategies: model the strategic parameters that control variation in evolution i e. the evolution of evolution Culture evolution: models the evolution of culture of a population and how the culture influences the evolution of individuals Co-evolution: individuals evolve through cooperation, or in competition with one other 2021/1/30 EIE426-AICV
2021/1/30 EIE426-AICV 5 Different ECs Several classes of EC algorithms have been developed: - Genetic algorithms (GA’s): model genetic evolution - Genetic programming: based on GA’s, but individuals are programs (represented as trees) - Evolutionary programming: from the simulation of adaptive behavior in evolution (phenotype evolution) - Evolution strategies: model the strategic parameters that control variation in evolution, i.e., the evolution of evolution - Culture evolution: models the evolution of culture of a population and how the culture influences the evolution of individuals. - Co-evolution: individuals evolve through cooperation, or in competition with one other