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A matrix formulation for an adaptive genetic algorithm is developed using mutation matrix and crossover matrix. Selection, mutation, and crossover are all parameter-free in the sense that the problem at a particular stage of evolution will choose the parameters automatically. This time dependent selection process was first developed in MOGA (mutation only… (More)

We investigate the statistical properties of two dimensional random cellular systems (froths) in term of their shell structure. The froth is analyzed as a system of concentric layers of cells around a given central cell. We derive exact analytical relations for the topological properties of the sets of cells belonging to these layers. Experimental… (More)

Investment strategies as rules for buy and sell are introduced as conditional statements involving inequalities of various moving averages. Different conditional statements on moving averages are represented as strings, en-codable as chromosomes in an approach based on genetic algorithm. The data mining of good investment strategies corresponds to the… (More)

The effect of random news on the performance of adaptive agents as investors in stock market is modelled by genetic algorithm and measured by their portfolio values. The agents are defined by the rules evolved from a simple genetic algorithm, based on the rate of correct prediction on past data. The effects of random news are incorporated via a model of… (More)

A new adaptive genetic algorithm using mutation matrix is introduced and implemented in a single computer using the quasi-parallel time sharing algorithm for the solution of the zero/one knapsack problem. The mutation matrix M (t) is constructed using the locus statistics and the fitness distribution in a population A(t) with N rows and L columns, where N… (More)

- K Y Szeto
- 1996

1996 PACS numbers: 03.