Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Niching methods for genetic algorithms
Niching methods for genetic algorithms
The theory of evolution strategies
The theory of evolution strategies
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic algorithms using low-discrepancy sequences
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
DCMA: yet another derandomization in covariance-matrix-adaptation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Covariance Matrix Adaptation Revisited --- The CMSA Evolution Strategy ---
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
An informational approach to the global optimization of expensive-to-evaluate functions
Journal of Global Optimization
A hybrid meta-heuristic for global optimisation using low-discrepancy sequences of points
Computers and Operations Research
Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms
Algorithmica - Including a Special Section on Genetic and Evolutionary Computation; Guest Editors: Benjamin Doerr, Frank Neumann and Ingo Wegener
Good permutations for deterministic scrambled Halton sequences in terms of L2-discrepancy
Journal of Computational and Applied Mathematics
Log(λ) modifications for optimal parallelism
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Algorithms (x, sigma, eta): quasi-random mutations for evolution strategies
EA'05 Proceedings of the 7th international conference on Artificial Evolution
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Multi-Modal Optimization (MMO) is ubiquitous in engineering, machine learning and artificial intelligence applications. Many algorithms have been proposed for multimodal optimization, and many of them are based on restart strategies. However, only few works address the issue of initialization in restarts. Furthermore, very few comparisons have been done, between different MMO algorithms, and against simple baseline methods. This paper proposes an analysis of restart strategies, and provides a restart strategy for any local search algorithm for which theoretical guarantees are derived. This restart strategy is to decrease some 'step-size', rather than to increase the population size, and it uses quasi-random initialization, that leads to a rigorous proof of improvement with respect to random restarts or restarts with constant initial step-size. Furthermore, when this strategy encapsulates a (1+1)-ES with 1/5th adaptation rule, the resulting algorithm outperforms state of the art MMO algorithms while being computationally faster.