An Introduction to Genetic Algorithms for Numerical Optimization

Abstract

The paper is organized as follows. Section 1 establishes the distinction between local and global optimization and the meaning of performance measures in the context of global optimization. Section 2 introduces the general idea of a genetic algorithm, as inspired from the biological process of evolution by means of natural selection. Section 3 provides a detailed comparison of the performance of three genetic algorithm-based optimization schemes against iterated hill climbing using the simplex method. Section 4 describes in full detail the use of a genetic algorithm to solve a real data modeling problem, namely the determination of orbital elements of a binary star system from observed radial velocities. The paper closes in section 5 with reflections on matters of a somewhat more philosophical nature, and includes a list of suggested further readings.