Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

Maesani, Andrea; Floreano, Dario

doi:10.1007/978-3-319-10762-2_27

Cited by 5 publications

(14 citation statements)

References 21 publications

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“…However, the method works only when an initial feasible solution is provided. In [45] we extended this method to allow its use also when started from infeasible solutions, by taking inspiration from Viability Evolution principles [46], [47]. Viability Evolution is an alternative abstraction of artificial evolution that operates by eliminating individuals not satisfying a set of criteria.…”

Section: Introductionmentioning

confidence: 99%

“…These criteria, called viability boundaries, are defined on the problem objectives or constraints, and are adapted during evolution. Similar to what is done in a Viability Evolution algorithm, in [45] the viability boundaries defined on the constraints of the problem are relaxed or tightened to drive the search towards feasible areas. This method, called (1+1)-ViE-CMA-ES, or in short (1+1)-ViE, was also enriched with a novel mechanism to adapt the step-size based on information collected at each constraint violation.…”

Section: Introductionmentioning

confidence: 99%

“…One possible way to partially overcome these limitations is to reformulate the rules for covariance matrix [43] and stepsize adaptation [45] designed for (1+1)-CMA-ES into rules for (µ, λ)-CMA-ES. However, this solution would still rely on a single search distribution, thus having limited capability of exploring disconnected feasible areas.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we explored this latter idea by proposing a novel memetic computing approach, called memetic Viability Evolution (mViE), that uses several local search units, constituted by (1+1)-ViE [45], that can be recombined by Differential Evolution (DE) [48] evolutionary operators. Differential Evolution was chosen as global search operator due to its intrinsic ability of adjusting the effect of mutations during the search.…”

Section: Introductionmentioning

confidence: 99%

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Memetic Viability Evolution for Constrained Optimization

Maesani

Iacca

Floreano

2016

IEEE Trans. Evol. Computat.

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The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation Evolution Strategy is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from Memetic Computing, i.e. the harmonious combination of multiple units of algorithmic information, and Viability Evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability Evolution emphasizes elimination of solutions not satisfying viability criteria, defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on Covariance Matrix Adaptation Evolution Strategy, towards feasible regions. These units can be recombined by means of Differential Evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Memetic Viability Evolution for Constrained Optimization

Maesani

Iacca

Floreano

2016

IEEE Trans. Evol. Computat.

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“…In [23], a novel variant of invasive weed optimization was combined as a local refinement procedure within differential evolution [23]. The combination of variability evolution [36] and CMA-ES [37] was proposed in [38] for the NLP.…”

mentioning

confidence: 99%

A multi-cycled sequential memetic computing approach for constrained optimisation

Sun

Garibaldi

Zhang

et al. 2016

Information Sciences

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Abdallah (2016) A multicycled sequential memetic computing approach for constrained optimisation. Information Sciences, 340-341 . pp. 175-190. ISSN 1872-6291 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/35193/1/ICMA_FinalVersion.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the Creative Commons Attribution Non-commercial No Derivatives licence and may be reused according to the conditions of the licence. For more details see: http://creativecommons.org/licenses/by-nc-nd/2.5/ A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractIn this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles.At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles.Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded.In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles.The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions.The comparison against some well-known algorithms showed the superiority of the de- * Corresponding author Email address: j.sun@greenwich.ac.uk (Jianyong Sun)Preprint submitted to Information Sciences July 19, 2016 veloped algorithm in terms of the consumed fitness evaluations and the solution quality.

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Disentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies

Tamò

Maesani

Träger

et al. 2017

Sci Rep

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Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.

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Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

Cited by 5 publications

References 21 publications

Memetic Viability Evolution for Constrained Optimization

Memetic Viability Evolution for Constrained Optimization

A multi-cycled sequential memetic computing approach for constrained optimisation

Disentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies

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