MOBAIS: A Bayesian Artificial Immune System for Multi-Objective Optimization

Castro, Pablo A. D.; Zuben, Fernando J. Von

doi:10.1007/978-3-540-85072-4_5

Cited by 20 publications

(17 citation statements)

References 12 publications

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“…These inherent advantages presented by some immuneinspired algorithms have also leaded to the development of several Multi-objective Artificial Immune Systems (MOAIS) algorithms (see, for example, [14,52,53,54,55,56]). 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.…”

Section: Evolutionary and Immune-inspired Multi-objective Algorithmsmentioning

confidence: 99%

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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Section: Evolutionary and Immune-inspired Multi-objective Algorithmsmentioning

confidence: 99%

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

Coelho

Silva

Zuben

2010

Neural Comput & Applic

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“…For instance, the Bayesian Artificial Immune System (BAIS) [11] [13] [14] and Multi-objective Bayesian Artificial Immune System (MOBAIS) [15][16] [17] for single and multi objective optimization in discrete domains, respectively, and the the Gaussian Artificial Immune System (GAIS) [18] and Multi-objective Gaussian Artificial Immune System (MO-GAIS) [19] for single and multi objective optimization in continuous domains, respectively. Besides the capability to deal with building blocks, these algorithms still preserve the aforementioned advantages of AISs.…”

Section: Introductionmentioning

confidence: 99%

Training multilayer perceptrons with a Gaussian Artificial Immune System

Castro

Zuben

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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In this paper we apply an immune-inspired approach to train Multilayer Perceptrons (MLPs) for classification problems. Our proposal, called Gaussian Artificial Immune System (GAIS), is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Gaussian network, representing the joint distribution of promising solutions. Subsequently, GAIS utilizes this probabilistic model for sampling new solutions. Thus, the algorithm takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions (building blocks). Besides the capability to identify and manipulate building blocks, the algorithm maintains diversity in the population, performs multimodal optimization and adjusts the size of the population automatically according to the problem. These attributes are generally absent from alternative algorithms, and all were shown to be useful attributes when optimizing the weights of MLPs, thus guiding to high-performance classifiers. GAIS was evaluated in six well-known classification problems and its performance compares favorably with that produced by contenders, such as opt-aiNet, IDEA and PSO.

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“…BAIS was also applied to perform feature selection using wrapper approach in [92,93]. The work in [91] extended BAIS for solving multi-objective Knapsack optimization problems. The corresponding proposal is called Multiobjective Bayesian Artificial Immune System (MOBAIS).…”

Section: Dendritic Cell Algorithmsmentioning

confidence: 99%

“…We believe that this work would be an important complement to [4,[23][24][25], where either the different aspects of the AIS field were surveyed or the recent advances in the AIS field have not been covered. Table 1 summarizes the main AIS algorithms, including the sections where [91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108] they are presented in this survey and a list of recent references. Among various mechanisms in the complex immune system that have been explored for AIS, self/nonself discrimination was one of the earlier mechanisms identified as a useful metaphor for computation and recently given continued attention.…”

Section: Recent Developments In Aismentioning

confidence: 99%

“…Hybrid approaches. By replacing the mutation and cloning operators with a probabilistic model, a Bayesian Artificial Immune System (BAIS) has been developed for efficiently solving hard optimization problems such as the effective handling of building blocks [92] and multiobjective optimization [91]. BAIS replaces the traditional mutation and cloning operators with a probabilistic model capable of properly capturing the most relevant interactions among the variables of the problem.…”

Section: Dendritic Cell Algorithmsmentioning

confidence: 99%

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