First-improvement vs. best-improvement local optima networks of NK landscapes

Authors:
Gabriela Ochoa;Sébastien Verel;Marco Tomassini
Affiliations:
School of Computer Science, University of Nottingham, Nottingham, UK;INRIA Lille - Nord Europe and University of Nice Sophia-Antipolis, France;Information Systems Department, University of Lausanne, Lausanne, Switzerland
Venue:
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Year:
2010

Citing 1
Cited 5

A study of NK landscapes' basins and local optima networks

Proceedings of the 10th annual conference on Genetic and evolutionary computation

Local optima networks and the performance of iterated local search

Proceedings of the 14th annual conference on Genetic and evolutionary computation
Local optima networks with escape edges

EA'11 Proceedings of the 10th international conference on Artificial Evolution
Hill-climbing strategies on various landscapes: an empirical comparison

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

Journal of Heuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepestascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.