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Exploring the Pareto frontier using multi-sexual evolutionary algorithms: an application to a flexible manufacturing problem

Exploring the Pareto frontier using multi-sexual evolutionar...

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Conference on Applications and Science of Neural Networks, Fuzzy Systems, and evolutionary Computation V

作者： Bonissone, S Subbu, R GE Co Global Res Ctr Schenectady NY 12309 USA

ISBN: (纸本)0819445541

In multi-objective optimization (MOO) problems we need to optimize or at least satisfy many possibly conflicting objectives. For instance, in manufacturing planning we might want to minimize the cost and production time while maximizing the product's quality. We propose the use of evolutionary algorithms (EAs) to solve these problems. EAs are computer programs that generate solutions by simulating a Darwinian evolution. Solutions are represented as individuals in a population and are assigned scores according to a fitness function that determines their relative quality. Strong solutions are selected for reproduction, and pass their genetic material to the next generation. Weak solutions are removed from the population. The fitness function evaluates each solution and returns a related score.. In MOO problems, this fitness function is vector-valued, i.e. it returns a value for each objective. Therefore, instead of a global optimum, we try to find the Pareto-optimal or non-dominated frontier. We use multi-sexual EAs with as many genders as optimization criteria. We have created new crossover and gender assignment functions, and experimented with various parameters to determine the best setting (yielding the highest number of non-dominated solutions.) These experiments are conducted using a variety of fitness functions, and the algorithms are later evaluated on a flexible manufacturing problem with total cost and time minimization objectives.

关键词： multi-objective evolutionary algorithms multi-sexual evolutionary algorithms Pareto frontier flexible manufacturing

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evolutionary algorithms for multi-objective optimization: Fuzzy preference aggregation and multi-sexual EAs

Evolutionary algorithms for multi-objective optimization: Fu...

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4th International Conference on Applications and Science of Neural Networks, Fuzzy Systems, and evolutionary Computation

作者： Bonissone, SR GE Co Ctr Corp Res & Dev Schenectady NY 12301 USA

ISBN: (纸本)0819441937

There are many approaches to solving multi-objective optimization problems using evolutionary algorithms. We need to select methods for representing and aggregating preferences, as well as choosing strategies for searching in multidimensional objective spaces. First we suggest the use of linguistic variables to represent preferences and the use of fuzzy rule systems to implement tradeoff aggregations. After a review of alternatives EA methods for multi-objective optimizations, we explore the use of multi-sexual genetic algorithms (MSGA). In using a MSGA, we need to modify certain parts of the GAs, namely the selection and crossover operations. The selection operator groups solutions according to their gender tag to prepare them for crossover. The crossover is modified by appending a gender tag at the end of the chromosome. We use single and double point crossovers. We determine the gender of the offspring by the amount of genetic material provided by each parent. The parent that contributed the most to the creation of a specific offspring determines the gender that the offspring will inherit. This is still a work in progress, and in the conclusion we examine many future extensions and experiments.

关键词： multi-objective evolutionary algorithms multi-sexual evolutionary algorithms preference aggregation fuzzy rule based systems

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