omni-aiNet: An Immune-Inspired Approach for Omni Optimization

Coelho, Guilherme Palermo; Zuben, Fernando J. Von

doi:10.1007/11823940_23

Cited by 55 publications

(51 citation statements)

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“…In contrast to evolutionary algorithms AIS feature a variable population size and they also have some inherent mechanisms for diversity maintenance, which makes them a promising technique for level set approximation [CZ06].…”

Section: Discussionmentioning

confidence: 99%

Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysis

Liu

Basto‐Fernandes

Yevseyeva

et al. 2017

Natural and Artificial Computation for Biomedicine and Neuroscience

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Abstract. The aim of evolutionary level set approximation is to find a finite representation of a level set of a given black box function. The problem of level set approximation plays a vital role in solving problems, for instance in fault detection in water distribution systems, engineering design, parameter identification in gene regulatory networks, and in drug discovery. The goal is to create algorithms that quickly converge to feasible solutions and then achieve a good coverage of the level set. The population based search scheme of evolutionary algorithms makes this type of algorithms well suited to target such problems. In this paper, the focus is on continuous black box functions and we propose a challenging benchmark for this problem domain and propose dual mutation strategies, that balance between global exploration and local refinement. Moreover, the article investigates the role of different indicators for measuring the coverage of the level set approximation. The results are promising and show that even for difficult problems in moderate dimension the proposed evolutionary level set approximation algorithm (ELSA) can serve as a versatile and robust meta-heuristic.

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Section: Discussionmentioning

confidence: 99%

Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysis

Liu

Basto‐Fernandes

Yevseyeva

et al. 2017

Natural and Artificial Computation for Biomedicine and Neuroscience

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“…One of these immune-inspired algorithms was proposed not only to solve multi-objective optimization problems (both uni and multi-modal), but also uni and multi-modal single-objective ones, without the need of any modifications to adjust the algorithm to the kind of problem being treated. This algorithm was denoted omni-aiNet (Artificial Immune Network for Omni Optimization [15]) and presents a joint flexibility given by the conjunction of the principles of the original omnioptimizer proposed by Deb and Tiwari [57] with the intrinsic advantages of AISs over other population-based strategies.…”

Section: Evolutionary and Immune-inspired Multi-objective Algorithmsmentioning

confidence: 99%

“…4, in this work, we explore the advantages of an algorithmic procedure to reconstruct phylogenies (the Neighbor Joining, to be described in Sect. 2.1.1) together with a search-based procedure, more specifically an immune-inspired algorithm, originally developed to multi-objective optimization (the omni-aiNet algorithm [15]), to evolve a population of unrooted phylogenetic trees, trying to minimize at the same time the minimum evolution and the mean-squared error from a given distance matrix.…”

Section: Classical Approaches To Phylogenetic Tree Reconstructionmentioning

confidence: 99%

“…Therefore, in this work, the multi-objective optimization will be performed by an immune-inspired approach derived from the omni-aiNet algorithm [15], which is a high-performance population-based approach that presents the aforementioned characteristics. The methodology adopted in this work has already been proposed by the authors in a preliminary work [16], and here, it will be improved and described with further details, and a more extensive set of experiments will be outlined.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An immune-inspired multi-objective approach to the reconstruction of phylogenetic trees

Coelho

Silva

Zuben

2010

Neural Comput & Applic

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This work presents the application of the omniaiNet algorithm-an immune-inspired algorithm originally developed to solve single and multi-objective optimization problems-to the reconstruction of phylogenetic trees. The main goal here is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time two optimization criteria: the minimum evolution and the mean-squared error. This proposal generates, in a single run, a set of nondominated solutions that represent the trade-offs of the two conflicting objectives, and gives the user the possibility of having distinct explanations for the differences observed at the terminal nodes of the trees. A series of experimental results is also reported in this work, in order to illustrate the effectiveness of the proposal and its capability to overcome the restrictive feedback provided by the application of well-known algorithms for phylogenetic reconstruction, such as the Neighbor Joining. Besides, the methodology presented in this work is compared to the popular NSGA-II algorithm, also modified to solve phylogenetic reconstruction problems.

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“…In [57], omni-aiNet was developed to solve single and multi-objective optimization problems, either with single and multi-global solutions. omni-aiNet united the concepts of omnioptimization with distinctive procedures associated with immuneinspired concepts and thus showed several advantages: (1) automatically adapting the exploration of the search space according to the intrinsic demand of the optimization problem; (2) adjusting its size during the execution of the algorithm, according to a predefined suppression threshold; (3) controlling the spread of solutions in the objective space with a new grid mechanism.…”

Section: Artificial Immune Networkmentioning

confidence: 99%