Genetic Algorithms and Their Applications: "Proceedings of the Second International Conference on Genetic Algorithms : July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, Ma"

Genetic Algorithms and Their Applications: "Proceedings of the Second International Conference on Genetic Algorithms : July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, Ma"

Genetic Algorithms and Their Applications: "Proceedings of the Second International Conference on Genetic Algorithms : July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, Ma"

Genetic Algorithms and Their Applications: "Proceedings of the Second International Conference on Genetic Algorithms : July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, Ma"

Excerpt

This paper investigates the use of diploid representations and dominance operators in genetic algorithms (GAs) to improve performance in environments that vary with time. The mechanics of diploidy and dominance in natural genetics are briefly discussed, and the usage of these structures and operators in other GA investigations is reviewed. An extension of the schema theorem is developed which illustrates the ability of diploid GAs with dominance to hold alternative alleles in abeyance. Both haploid and diploid GAs are applied to a simple time varying problem: an oscillating, blind knapsack problem. Simulation results show that a diploid GA with an evolving dominance map adapts more quickly to the sudden changes in this problem environment than either a haploid GA or a diploid GA with a fixed dominance map. These proof-of-principle results indicate that diploidy and dominance can be used to induce a form of long term distributed memory within a population of structures.

Real world problems are seldom independent of time. If you don't like the weather, wait five minutes and it will change. If this week gasoline costs $1.30 a gallon, next week it may cost $0.89 a gallon or perhaps $2.53 a gallon. In these and many more complex ways, real world environments are both nonstationary and noisy. Searching for good solutions or good behavior under such conditions is a difficult task; yet, despite the perpetual change and uncertainty, all is not lost. History does repeat itself, and what goes around does come around. The horrors of Malthusian extrapolation rarely come to pass, and solutions that worked well yesterday are at least somewhat likely to be useful when circumstances are somewhat similar tomorrow or the day after. The temporal regularity implied in these observations places a premium on search augmented by selective memory. In other words, a system which does not learn the lessons . . .

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