Evolutionary Computing Chapter 15 Chapter 15 CoEvolutionary Systems

Evolutionary Computing Chapter 15

Chapter 15: Co-Evolutionary Systems • Motivation • Cooperative vs Competitive coevolution • Approaches 1 /6

Motivation • So far: problems with easy to measure fitness • More difficult: fitness depending on context: – Solution represents a strategy that works in opposition to some competitor (chess) – Solution being evolved does not represent a complete solution (set of traffic-light controllers) • In nature: adaptation of a biological species – Adaptive value is determined by the evolutionary niche which is determined by the other organisms: • Positive effect (mutualism/symbiosis), e. g. plants and insects • Negative effect (predation/parasitism), e. g. foxes and rabbits – Coevolution: the landscape “seen” by each species is affected by the configuration of all other interacting species and will move 2 /6

Coevolution • Applications: – Coevolution of a population of partial models in Michigan-style LCS – Evolution of game playing strategies • Implementations: – Both cooperative and competitive – Using both single and multiple species models 3 /6

Cooperative Coevolution • Models in which a number of different species, each representing part of a problem, cooperate in order to solve a larger problem – High dimensional function optimisation – Job shop scheduling • Advantage: effective function decomposition • Disadvantage: relies on user to subdivide problem • Difficulty: how to pair the populations to gain a fitness value 4 /6

Competitive Coevolution • Model where individuals compete againts each other to gain fitness at each other’s expense – Iterated prisoner’s dilemma 5 /6

Algorithmic Adaptations • Choice of context can significantly influence the EA performance • Adaptations include: – Averaging over more contexts – Include history (archive of past solutions) to avoid “cycling” 6 /6