Multi-Objective GRASP for Maximizing Diversity

A multi-objective GRASP for partial classification

Metaheuristic algorithms have been used successfully in a number of data mining contexts and specifically in the production of classification rules. Classification rules describe a class of interest or a subset of this class, and as such may also be used as an aid in prediction. The production and selection of classification rules for a particular class of the database is often referred to as p...

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a du...

Multi-objective Uniform-diversity Genetic Algorithm (MUGA)

Maximizing Diversity for Multimodal Optimization

Most multimodal optimization algorithms use the so called niching methods [1] in order to promote diversity during optimization, while others, like Artificial Immune Systems [2] try to find multiple solutions as its main objective. One of such algorithms, called dopt-aiNet [3], introduced the Line Distance that measures the distance between two solutions regarding their basis of attraction. In ...

An Elitist GRASP Metaheuristic for the Multi-objective Quadratic Assignment Problem

We propose an elitist Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm, called mGRASP/MH, for approximating the Pareto-optimal front in the multi-objective quadratic assignment problem (mQAP). The proposed algorithm is characterized by three features: elite greedy randomized construction, adaptation of search directions and cooperation between solutions. The approach ...