ACO beats EA on a dynamic pseudo-boolean function

Authors:
Timo Kötzing;Hendrik Molter
Affiliations:
Max Planck Institute for Informatics, Saarbrücken, Germany;Saarland University, Saarbrücken, Germany
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Year:
2012

Citing 12
Cited 2

MAX-MIN Ant system

Future Generation Computer Systems
Theoretical analysis of a mutation-based evolutionary algorithm for a tracking problem in the lattice

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the runtime analysis of the 1-ANT ACO algorithm

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Runtime Analysis of a Simple Ant Colony Optimization Algorithm

Algorithmica
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analysis of the (1+1) EA for a dynamically bitwise changing ONEMAX

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A few ants are enough: ACO with iteration-best update

Proceedings of the 12th annual conference on Genetic and evolutionary computation
Drift analysis with tail bounds

PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation

Algorithmica - Special Issue: Theory of Evolutionary Computation
Simple max-min ant systems and the optimization of linear pseudo-boolean functions

Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
The use of tail inequalities on the probable computational time of randomized search heuristics

Theoretical Computer Science
Evolutionary optimization in uncertain environments-a survey

IEEE Transactions on Evolutionary Computation

Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Runtime analysis of ant colony optimization on dynamic shortest path problems

Proceedings of the 15th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we contribute to the understanding of the behavior of bio-inspired algorithms when tracking the optimum of a dynamically changing fitness function over time. In particular, we are interested in the difference between a simple evolutionary algorithm (EA) and a simple ant colony optimization (ACO) system on deterministically changing fitness functions, which we call dynamic fitness patterns. Of course, the algorithms have no prior knowledge about the patterns. We construct a bit string optimization problem where we can show that the ACO system is able to follow the optimum while the EA gets lost.