Intelligent Genetic Algorithms in Evolutionary Computation Part 1. Biological Foundation

In this paper, we review a large amount of historical biological literature [Darwin, 1862, 1871; Fisher, 1930 and others] and recent developments in biological [ Anderson, 1994] and biocomputational literature [Miller & Todd, 1992, 1994], try to integrate the dynamics of interplay between natural selection and sexual selection through mate choice in biology with evolutionary computation as a process of search, diversification and optimization and originate a new class of evolutionary algorithm which we term Intelligent Genetic Algorithms. These intelligent genetic algorithms demonstrate their effectiveness and efficiency in generating evolutionary innovations, maintaining genetic diversity, promoting mate choice and sexual recombination in species and guiding the movement of a population from local optima to global optima in parallel. Furthermore, we attempt to provide some common biological origins for these new Intelligent Genetic Algorithms

Multi-agent system for dynamic manufacturing system optimization

This paper deals with the application of multi-agent system concept for optimization of dynamic uncertain process. These problems are known to have a computationally demanding objective function, which could turn to be infeasible when large problems are considered. Therefore, fast approximations to the objective function are required. This paper employs bundle of intelligent systems algorithms tied together in a multi-agent system. In order to demonstrate the system, a metal reheat furnace scheduling problem is adopted for highly demanded optimization problem. The proposed multi-agent approach has been evaluated for different settings of the reheat furnace scheduling problem. Particle Swarm Optimization, Genetic Algorithm with different classic and advanced versions: GA with chromosome differentiation, Age GA, and Sexual GA, and finally a Mimetic GA, which is based on combining the GA as a global optimizer and the PSO as a local optimizer. Experimentation has been performed to validate the multi-agent system on the reheat furnace scheduling problem

Overcoming Problems in the Measurement of Biological Complexity

In a genetic algorithm, fluctuations of the entropy of a genome over time are interpreted as fluctuations of the information that the genome's organism is storing about its environment, being this reflected in more complex organisms. The computation of this entropy presents technical problems due to the small population sizes used in practice. In this work we propose and test an alternative way of measuring the entropy variation in a population by means of algorithmic information theory, where the entropy variation between two generational steps is the Kolmogorov complexity of the first step conditioned to the second one. As an example application of this technique, we report experimental differences in entropy evolution between systems in which sexual reproduction is present or absent.Comment: 4 pages, 5 figure

Fast and optimal broad-band Stokes/Mueller polarimeter design by the use of a genetic algorithm

A fast multichannel Stokes/Mueller polarimeter with no mechanically moving parts has been designed to have close to optimal performance from 430-2000 nm by applying a genetic algorithm. Stokes (Mueller) polarimeters are characterized by their ability to analyze the full Stokes (Mueller) vector (matrix) of the incident light. This ability is characterized by the condition number, $\kappa$, which directly influences the measurement noise in polarimetric measurements. Due to the spectral dependence of the retardance in birefringent materials, it is not trivial to design a polarimeter using dispersive components. We present here both a method to do this optimization using a genetic algorithm, as well as simulation results. Our results include fast, broad-band polarimeter designs for spectrographic use, based on 2 and 3 Ferroelectric Liquid Crystals, whose material properties are taken from measured values. The results promise to reduce the measurement noise significantly over previous designs, up to a factor of 4.5 for a Mueller polarimeter, in addition to extending the spectral range.Comment: 10 pages, 6 figures, submitted to Optics Expres

When Darwin Met Einstein: Gravitational Lens Inversion with Genetic Algorithms

Gravitational lensing can magnify a distant source, revealing structural detail which is normally unresolvable. Recovering this detail through an inversion of the influence of gravitational lensing, however, requires optimisation of not only lens parameters, but also of the surface brightness distribution of the source. This paper outlines a new approach to this inversion, utilising genetic algorithms to reconstruct the source profile. In this initial study, the effects of image degradation due to instrumental and atmospheric effects are neglected and it is assumed that the lens model is accurately known, but the genetic algorithm approach can be incorporated into more general optimisation techniques, allowing the optimisation of both the parameters for a lensing model and the surface brightness of the source.Comment: 9 pages, to appear in PAS

Genetic programming: the ratio of crossover to mutation as a function of time

This article studies the sub-tree operators: mutation and crossover, within the context of Genetic Programming. Two standard problems, symbolic linear regression and a non-linear tree, were presented to the algorithm at each stage. The behaviour of the operators in regard to fitness is first established, followed by an analysis of the most optimal ratio between crossover and mutation. Subsequently, three algorithms are presented as candidates to dynamically learn the most optimal level of this ratio. The results of each algorithm are then compared to each other and the traditional constant ratio