A genetic algorithm is applied to the task of designing binary phase-only filters in a pattern recognition application. Binary phase-only filters have traditionally been using the classical matched filter as a baseline and then setting the magnitude portion of the filter to unity and binarizing the phase information. The resulting filter has much of its original information content, but is represented with a greatly reduced set of elements. Such filters have been shown to exceed the pattern recognition ability of the classical matched filter on which they are based. However, binary phase-only filters designed using this method are not optimal for discrimination or invariance to pattern changes and several different researchers have investigated various optimization techniques. This paper describes a new technique for designing binary phase-only filters using a genetic algorithm. A population of filters is initially constructed with random phase elements and then modified by the genetic algorithm to produce successively better filters. Each member of the population consists of two chromosomes which contain the genetic information coding for a paid of discrimination filters. During each generation of the algorithm, a new population is produced from the previous population by applying a set of four operators. The four operators include a stochastic remainder selection operator, a two-dimensional crossover operator, a mutation operator, and a survival operator. The fitness function used in the selection and survival operators is based on the ability of the two binary phase-only filters represented by an individual's chromosomes to discriminate between two different classes of characters.
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