Application of Genetic Optimization Algorithm in Financial Portfolio Problem

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Control parameters</td><td class="align_center">Meanings</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">String length (<i>L</i>)</td><td class="align_center">The length of each code, including fixed length and variable length</td></tr><tr><td class="align_left">Population capacity</td><td class="align_center">Number of individuals per generation</td></tr><tr><td class="align_left">Crossover rate</td><td class="align_center">The probability of executing the crossover operator is recorded as PC</td></tr><tr><td class="align_left">Variation rate</td><td class="align_center">The probability of executing the mutation operator is recorded as PM</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Common parameters of genetic algorithm.</div>

Computational Intelligence and Neuroscience

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Table 1

Table 1: Application of Genetic Optimization Algorithm in Financial Portfolio Problem 