Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
The theory of evolution strategies
The theory of evolution strategies
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Analysis of game playing agents with fingerprints
Analysis of game playing agents with fingerprints
Understanding representational sensitivity in the iterated prisoner's dilemma with fingerprints
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Graph-based evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Fingerprinting: Visualization and Automatic Analysis of Prisoner's Dilemma Strategies
IEEE Transactions on Evolutionary Computation
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The representation of a problem for evolutionary computation is the choice of the data structure used for solutions and the variation operators that act upon that data structure. For a difficult problem, choosing a good representation can have an enormous impact on the performance of the evolutionary computation system. To understand why this is so, one must consider the search space and the fitness landscape induced by the representation. If someone speaks of the fitness landscape of a problem, they have committed a logical error: problems do not have a fitness landscape. The data structure used to represent solutions for a problem in an evolutionary algorithm establishes the set of points in the search space. The topology or connectivity that joins those points is induced by the variation operators, usually crossover and mutation. Points are connected if they differ by one application of the variation operators. Assigning fitness values to each point makes this a fitness landscape. The question of the type of fitness landscape created when a representation is chosen is a very difficult one, and we will explore it in this chapter.