Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
From Twomax To The Ising Model: Easy And Hard Symmetrical Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Real royal road functions for constant population size
Theoretical Computer Science
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Real royal road functions: where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
The one-dimensional Ising model: mutation versus recombination
Theoretical Computer Science
Effects of diversity control in single-objective and multi-objective genetic algorithms
Journal of Heuristics
Rigorous analyses of simple diversity mechanisms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Memetic algorithms with variable-depth search to overcome local optima
Proceedings of the 10th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Memetic algorithms with variable-depth search to overcome local optima
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Why and how to measure exploration in behavioral space
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An analysis of multi-chromosome GAs in deceptive problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
A large population size can be unhelpful in evolutionary algorithms
Theoretical Computer Science
A framework for evolutionary algorithms based on Charles Sanders Peirce's evolutionary semiotics
Information Sciences: an International Journal
Hybridizing evolutionary algorithms with opportunistic local search
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
| Hi-index | 0.00 |
Maintaining diversity is important for the performance of evolutionary algorithms. Diversity mechanisms can enhance global exploration of the search space and enable crossover to find dissimilar individuals for recombination. We focus on the global exploration capabilities of mutation-based algorithms. Using a simple bimodal test function and rigorous runtime analyses, we compare well-known diversity mechanisms like deterministic crowding, fitness sharing, and others with a plain algorithm without diversification. We show that diversification is necessary for global exploration, but not all mechanisms succeed in finding both optima efficiently.