0. Evolutionary Computing
Chapter 15

1. Chapter 15: Co-Evolutionary Systems
Motivation
Cooperative vs Competitive coevolution
Approaches

2. 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

3. Coevolution Applications: Implementations:
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

4. 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

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

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”