An analysis of the behavior of simplified evolutionary algorithms on trap functions

Authors:
S. Nijssen;T. Back
Affiliations:
Leiden Inst. of Adv. Comput. Sci., Leiden Univ., Netherlands;-
Venue:
IEEE Transactions on Evolutionary Computation
Year:
2003

Citing 0
Cited 9

Analysis of an evolutionary algorithm with HyperMacromutation and stop at first constructive mutation heuristic for solving trap functions

Proceedings of the 2006 ACM symposium on Applied computing
Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Engineering Applications of Artificial Intelligence
Real options approach to evaluating genetic algorithms

Applied Soft Computing
A practical guide for using statistical tests to assess randomized algorithms in software engineering

Proceedings of the 33rd International Conference on Software Engineering
Clonal selection algorithm with dynamic population size for bimodal search spaces

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Clonal selection algorithms: a comparative case study using effective mutation potentials

ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
The use of tail inequalities on the probable computational time of randomized search heuristics

Theoretical Computer Science
Introduction of a mutation specific fast non-dominated sorting GA evolved for biochemical optimizations

SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Evolutionary algorithm characterization in real parameter optimization problems

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Methods are developed to numerically analyze an evolutionary algorithm (EA) that applies mutation and selection on a bit-string representation to find the optimum for a bimodal unitation function called a trap function. This research bridges part of the gap between the existing convergence velocity analysis of strictly unimodal functions and global convergence results assuming the limit of infinite time. As a main result of this analysis, a new so-called (1 : λ)-EA is proposed, which generates offspring using individual mutation rates pi. While a more traditional EA using only one mutation rate is not able to find the global optimum of the trap function within an acceptable (nonexponential) time, our numerical investigations provide evidence that the new algorithm overcomes these limitations. The analysis tools used for the analysis, based on absorbing Markov chains and the calculation of transition probabilities, are demonstrated to provide an intuitive and useful method for investigating the capabilities of EAs to bridge the gap between a local and a global optimum in bimodal search spaces.