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Evolution Strategies-4

DOI: 10.1016/b978-155860637-1/50017-5
  • Biology
  • Computer Science
  • Mathematics


Publisher Summary The terms “evolution” and “strategy” show that a detailed imitation of natural evolution principles is not of immediate importance in evolution strategies (ES). Computer simulations can provide new insights into complex interacting systems. The mechanisms of evolution in conjunction with their resulting dynamical processes are certainly among the most complex systems found in nature. Evolution strategies represent one of the three major early approaches to evolutionary computation, besides genetic algorithms and evolutionary programming. Evolution strategy experiments are restricted to variables with integer values, which represent parameters of an experimental setup to be optimized. Object parameters are modified by using the strategy parameters, mimicking the biological principle that an offspring's traits are usually similar to parental traits and that, generally, erratic and large mutations are less common than slight modifications. This chapter presents some examples to demonstrate ES recombinations and their different schemes—local and global, both with the object and strategy parameters combined and independent. Meta-evolution schemes resemble the evolution of natural populations isolated from one another for a certain period. Evolutionary algorithms take advantage of the inherent parallelism in populations searching for optimal regions in the phenotypical space.

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