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Crossover and evolutionary stability in the prisoner's dilemma
Xavier Thibert-Plante1, Paul Charbonneau
1Physics Department, Université de Montréal, Montréal, Québec, Canada. xavier.thibert-plante@mail.mcgill.ca
Evolutionary Computation
|August 21, 2007
Summary
Crossover in genetic algorithms enhances evolutionary stability in the iterated prisoner's dilemma by preventing predator strategies and shortening unstable periods. This genetic mechanism helps populations adapt and maintain long-term correlations.
Area of Science:
- Computational Intelligence
- Evolutionary Computation
- Game Theory
Background:
- Evolutionary simulations model strategy adaptation.
- The iterated prisoner's dilemma explores cooperation and defection.
- Genetic algorithms use operators like crossover for evolution.
Purpose of the Study:
- To investigate the specific role of crossover in evolutionary stability.
- To understand how crossover affects population dynamics during stable and unstable phases.
- To analyze the impact of crossover on predator-prey dynamics in evolutionary simulations.
Main Methods:
- Utilizing genetic algorithm-based evolutionary simulations.
- Modeling the iterated prisoner's dilemma with evolving strategies.
- Analyzing population stability, strategy transitions, and predator emergence.
- Examining the influence of crossover on evolutionary dynamics and gene pool heterogeneity.
Main Results:
- Crossover significantly enhances overall evolutionary stability.
- Crossover impedes the emergence and spread of predator strategies during stable phases.
- Crossover reduces the duration of unstable evolutionary epochs by facilitating the rediscovery of prior strategies.
- Crossover sustains long-range temporal correlations by preserving useful building blocks in heterogeneous gene pools.
Conclusions:
- Crossover is crucial for maintaining evolutionary stability, not just for improving average scores.
- The mechanism involves both preventing disruptive strategies and accelerating recovery from instability.
- Crossover's role in preserving genetic diversity and building blocks is key to long-term evolutionary success.
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