Multi-Objectivization in Genetic Algorithms
Darrell F. Lochtefeld · OhioLink ETD Center (Ohio Library and Information Network) · 2011
Multi-Objectivization in Genetic AlgorithmsMulti-objectivization is the process of reformulating a single-objective problem into a multiobjective problem and solving it with a multi-objective method in order to provide a solution to the original single-objective problem.Multi-objectivization differs from other traditional divideand-conquer techniquesthe method splits the objective function rather than the search space.Prior to recent evidence, such reformulations were thought to make a problem more complex.However, more recent research suggests that decomposition in the objective space can lead to useful optimization techniques when coupled with a population-based search.Machine based optimization techniques are varied but often based upon an analogy to realworld phenomena.A family and sub-family of algorithms called evolutionary and genetic algorithms respectively are inspired by Darwin's survival-of-the-fittest theory and the concept of modeling evolutionary process.These algorithms manage a population of solutions in a global search process that recombines and creates new solutions in order to generate useful solutions to hard problems.A sub-type of Evolutionary Algorithms (EAs) called Multiple Objective Evolutionary Algorithms (MOEAs) are designed to search for solutions to problems formulated with multiple-objectives.Multiple-objective problems have two or more partially competing objectives such as minimization of cost and maximization of safety.The application of MOEAs to solve problems that in their most natural formulation have a single objective is relatively new.This work investigates Genetic Algorithms (GAs) and their close relatives MOEAs in both a general categorical sense and as they are applied to multi-objectivization.A diversity