Multivariate multi-model approach for globally multimodal problems

Chuang, Chung-Yao; Hsu, Wen-Lian

doi:10.1145/1830483.1830544

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…A similar setting has been considered by Chuang and Hsu [3], who introduce an EDA that is also specifically tailored toward multimodal optimization. However, they evaluate their results only on trap functions with a low number of optima (two to four).…”

Section: Introductionmentioning

confidence: 99%

Bivariate estimation-of-distribution algorithms can find an exponential number of optima

Doerr

Krejca

2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms (EAs) typically converge only to a single solution. While this can be counteracted by applying niching strategies, the number of optima is nonetheless trivially bounded by the population size.Estimation-of-distribution algorithms (EDAs) are an alternative, maintaining a probabilistic model of the solution space instead of an explicit population. Such a model is able to implicitly represent a solution set that is far larger than any realistic population size.To support the study of how optimization algorithms handle large sets of optima, we propose the test function EqalBlocks-OneMax (EBOM). It has an easy to optimize fitness landscape, however, with an exponential number of optima. We show that the bivariate EDA mutual-information-maximizing input clustering (MIMIC), without any problem-specific modification, quickly generates a model that behaves very similarly to a theoretically ideal model for that function, which samples each of the exponentially many optima with the same maximal probability.

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Section: Introductionmentioning

confidence: 99%

Bivariate estimation-of-distribution algorithms can find an exponential number of optima

Doerr

Krejca

2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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“…In addition to this, niching method could promote population diversity and at the same time increase the efficiency [22]. Chuang and Hsu [22] put forward a multivariate multimodel approach equip a heuristic mechanism to choose the number of models.…”

Section: Niching Methodsmentioning

confidence: 99%

“…Chuang and Hsu [22] put forward a multivariate multimodel approach equip a heuristic mechanism to choose the number of models. Comparing to ECGA, the multivariate multimodel approach could obtain more global optima and reduce the number of generations to converge.…”

Section: Niching Methodsmentioning

confidence: 99%

Quantum-inspired evolutionary algorithm with linkage learning

Wang

Yuan

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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The quantum-inspired evolutionary algorithm (QEA) uses several quantum computing principles to optimize problems on a classical computer. QEA possesses a number of quantum individuals, which are all probability vectors. They work well for linear problems but fail on problems with strong interactions among variables. Moreover, many optimization problems have multiple global optima. And because of the genetic drift, these problems are difficult for evolutionary algorithms to find all global optima. Local and global migration that QEA uses to synchronize different individuals prevent QEA from finding multiple optima. To overcome these difficulties, we proposed a quantum-inspired evolutionary algorithm with linkage learning (QEALL). QEALL uses a modified conceptguide operator based on low order statistics to learn linkage. We also replaced the migration procedure by a niching technology to prevent genetic drift, accordingly to find all global optima and to expedite convergence speed. The performance of QEALL was tested on a number of benchmarks including both unimodal and multimodal problems. Empirical evaluation suggests that the proposed algorithm is effective and efficient.

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“…For ECGA this is particularly notable. The approach itself is known to have trouble working with certain multi-modal functions [2], and it seems NASBench 301 is among these problems. Even so, the asynchronous approach has runs in which a solution with at least the target fitness was found, whereas the synchronous approach does not.…”

Section: Nasbench 301mentioning

confidence: 99%

The Impact of Asynchrony on Parallel Model-Based EAs

Guijt¹,

Thierens²,

Alderliesten³

et al. 2023

Preprint

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In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators (GAs) are known to suffer from an evaluation time bias, which also influences the performance of the approach. Model-Based Evolutionary Algorithms (MBEAs) are more scalable than classic GAs by virtue of capturing the structure of a problem in a model. If this model is learned through linkage learning based on the population, the learned model may also capture biases. Thus, if an asynchronous parallel MBEA is also affected by an evaluation time bias, this could result in learned models to be less suited to solving the problem, reducing performance. Therefore, in this work, we study the impact and presence of evaluation time biases on MBEAs in an asynchronous parallelization setting, and compare this to the biases in GAs. We find that a modern MBEA, GOMEA, is unaffected by evaluation time biases, while the more classical MBEA, ECGA, is affected, much like GAs are. CCS CONCEPTS• Mathematics of computing → Evolutionary algorithms; • Theory of computation → Parallel computing models.

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Multivariate multi-model approach for globally multimodal problems

Cited by 7 publications

References 24 publications

Bivariate estimation-of-distribution algorithms can find an exponential number of optima

Bivariate estimation-of-distribution algorithms can find an exponential number of optima

Quantum-inspired evolutionary algorithm with linkage learning

The Impact of Asynchrony on Parallel Model-Based EAs

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