Publication

A New Clustering Evolutionary Multi-Objective Optimisation Technique

Daniel Favrat
2001
Conference Papers

Abstract

This paper introduces a multi-objective EA, termed the Clustering Pareto Evolutionary Algorithm (CPEA). The CPEA finds and retains many local Pareto- optimal fronts, rather than just the global front as is the case of most multi- objective EAs found in the literature. This has been achieved using a clustering technique commonly used in multivariate statistical analysis, which ensures that competition between individuals is local in variable space, allowing the population to grow to resolve as many Pareto-optimal fronts as necessary. The performance of the CPEA is evaluated on several test problems taken from the literature which have either single optima or multiple local optima and is shown to be extremely effective. The present clustering method is computationally expensive and will be replaced with an incremental method in the near future.

Official source

https://infoscience.epfl.ch/entities/publication/e685039a-84a0-42a2-942f-64a2a628f003

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Daniel Favrat
2001
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e685039a-84a0-42a2-942f-64a2a628f003

About this result

Related concepts (32)

Memetic algorithm

A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence. Memetic algorithms represent one of the recent growing areas of research in evolutionary computation.

K-means clustering

k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances.

Cluster analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.

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