An introduction to simulated evolutionary optimization

作者：David B. Fogel · 发表于：IEEE Transactions on Neural Networks · 年份：1994 · DOI：10.1109/72.265956 · 被引用次数：1516 · 研究领域：Metaheuristic Optimization Algorithms Research、Evolutionary Algorithms and Applications、Evolution and Genetic Dynamics

Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often outperform classical methods of optimization when applied to difficult real-world problems. There are currently three main avenues of research in simulated evolution: genetic algorithms, evolution strategies, and evolutionary programming. Each method emphasizes a different facet of natural evolution. Genetic algorithms stress chromosomal operators. Evolution strategies emphasize behavioral changes at the level of the individual. Evolutionary programming stresses behavioral change at the level of the species. The development of each of these procedures over the past 35 years is described. Some recent efforts in these areas are reviewed.