Showing papers on "Evolutionary computation published in 1991"

Meta-evolutionary programming

Abstract: A brief review of efforts is simulated evolution is given. Evolutionary programming is a stochastic optimization technique that is useful for discovering the extrema of a nonlinear function. To implement such a search, several high-level parameters must be chosen, such as the amount of mutational noise, the severity of the mutation noise, and so forth. The authors address incorporating a meta-level evolutionary programming that can simultaneously evolve optimal settings for these parameters while a search for the appropriate extrema is being conducted. The preliminary experiments reported indicate the suitability of such a procedure. Meta-evolutionary programming was able to converge to points on each of two response surfaces that were close to the global optimum. >

Genetic algorithms for numerical optimization

Abstract: Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known. However, such applications can encounter problems that sometimes delay, if not prevent, finding the optimal solutions with desired precision. In this paper we describe applications of GAs to numerical optimization, present three novel ways to handle such problems, and give some experimental results.

Parallel Genetic Algorithms Applied to the Traveling Salesman Problem

Abstract: Genetic algorithms are adaptive search algorithms that have been shown to be robust optimization algorithms for multimodal real-valued functions and a variety of combinatorial optimization problems. In contrast to more standard search algorithms, genetic algorithms base their progress on the performance of a population of candidate solutions, rather than on a single candidate solution.The authors will concentrate on the application of genetic algorithms to the traveling salesman problem. For this problem, there exist several such algorithms, ranging from pure genetic algorithms to genetic algorithms that incorporate heuristic information. These algorithms will be reviewed and their performance contrasted.A serious drawback of genetic algorithms is their inefficiency when implemented on a sequential machine. However, due to their inherent parallel properties, they can be successfully implemented on parallel machines, resulting in considerable speedup. Parallel genetic algorithms will be reviewed and their ...

Evolutionary methods for training neural networks

Abstract: Training neural networks by the implementation of a gradient-based optimization algorithm (e.g., back-propagation) often leads to locally optimal solutions which may be far removed from the global optimum. Evolutionary optimization methods offer a procedure to stochastically search for suitable weights and bias terms given a specific network topology. The topics discussed are evolutionary programming; genetic algorithms; evolutionary function optimization experiments; background to classification problems and experimental results with evolutionary training. >

Genetic algorithms: hints from biological science

Abstract: C.T. Walbridge (Technology Review, vol.92, p.46-53, (1989)) discussed how ideas from evolution theory can be used to help in solving problems often addressed by the traditional symbolic methods of artificial intelligence. Choosing VLSI design as an example, he suggested that a chip chromosome would evolve just as genes evolve and the surviving organisms would mate to produce offspring having the chromosomes for the most efficient chips. These result from a combining of different parts of different chromosomes in the offspring to preserve characteristics derived from their parents. The example of a basic square root computation serves to illustrate how the ideas from evolution theory and biological science can be used in the design of globally more efficient genetic algorithms. >