Genetic Algorithms. Overview “A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining.

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Presentation transcript:

Genetic Algorithms

Overview “A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states rather than by modifying a single state.” (Russel and Norvig, 126) Inspired by Darwinian Theory of Evolution

Abstract Let’s talk about Optimization and Constraints – What do we know?

Structure of GA’s Population – Generated randomly Fitness Function – Assess each individual Selection – Of genes for crossover Crossover – Allows for variety in gene pool Mutation – Low probability for random gene mutation

More Examples – Randomly generated 2D platforming cars N-Queens Problem Pokémon team optimization – (my SMP) Cycle graph

Practicality Powerful because of simplicity Held back by inefficiency When would we use a GA? – Optimization towards some constraint – Relationship with AI

Pokémon The individual is a team of six Pokémon Each Pokémon on a team is a gene The goal is to find the best team in terms of ability to win Assume optimal AI How do we structure this as a GA?