Parent Selection Strategies for Evolutionary Algorithms A Comparison of Parent Selection Strategies Modeled After Human Social Interaction By Michael Ames.

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Parent Selection Strategies for Evolutionary Algorithms A Comparison of Parent Selection Strategies Modeled After Human Social Interaction By Michael Ames

Motivation  Because existing research is limited, but shows significant improvement in algorithm robustness, additional research is warranted.  Evolutionary algorithms modeled after higher level human social interaction could produce significant improvement in convergence times and individual fitness.

Background  Assortative mating – mate selection based on the level of genetic similarities between mates.  Disassortative mating – mate selection based on the level of genetic dissimilarities between mates.

Background  Hamming distance - The number of bits which differ between two binary strings.  HD =

Goals  Determine difficulty of implementation.  Determine how well the strategies handle premature convergence.  Measure selective pressure.  Rate performance vs. other social strategies  Rate performance vs. existing strategies

Test Algorithms  N-Queens  Binary Knapsack

8-Queens Q Q Q Q Q Q Q Q

Binary Knapsack

Approach  Implement the evolutionary algorithms  Implement a separate mate selection object.

Results NQueen

Tournament – generations:3925 time: sec AMEA Marriage – generations: 5316 time: sec AMEA Low Cheat – generations: 4876 time: sec AMEA High Cheat – generations: 4215 time: sec

Results Knapsack

Tournament – generations:6975 best: AMEA Marriage – generations: 6350 best: AMEA Low Cheat – generations: 6550 best: AMEA High Cheat – generations: 6825 best:

Conclusions  Poor performance  Longer time / inferior results

Future Work  Develop other evolutionary mating strategies  Implement one of the algorithms with two separate populations dynamic in size, one designated male the other female, then apply the same or similar strategies

Any of these?