A new algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution

Trunfio, Giuseppe A.; Topa, Paweł; Wźs, Jarosław

doi:10.1016/j.ins.2016.08.080

Cited by 41 publications

(12 citation statements)

References 25 publications

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“…In general, the n-dimensional problem can be decomposed into m subproblems with same dimension, i.e., n m = n/m [64]. Following this, a CC technique can be formally defined as follows [65]:…”

Section: Cooperative Co-evolutionmentioning

confidence: 99%

Cooperative co-evolution for feature selection in Big Data with random feature grouping

et al. 2020

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A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity.

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Section: Cooperative Co-evolutionmentioning

confidence: 99%

Cooperative co-evolution for feature selection in Big Data with random feature grouping

et al. 2020

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“…Chen [33] proposed an adaptive resource allocation strategy for objective space partition-based multi-objective optimization, named OPE-MOEA; it firstly partitions the objective space into subspaces evenly and then defines a metric to measure the contributions of subspaces to the population convergence, and according to the contributions to allocate computational resources. In terms of decomposition of decision variables, some studies allocate the computational resources to different subgroups by measuring their improvement [34], contribution [35][36][37] and the importance degree of their decision variables [38].…”

Section: Resource Allocation Methodsmentioning

confidence: 99%

Preference-Inspired Coevolutionary Algorithm Based on Differentiated Resource Allocation Strategy

Qiu

Wang

et al. 2020

IEEE Access

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Preference-inspired co-evolutionary algorithms (PICEAs) consider the target vectors as the preferences, and then use the domination relationship between the candidate solutions and target vectors to increase their selection pressure. However, the size of dominating objective space varies with the different positions of candidate solutions and it leads to the imbalance of the evolutionary ability of whole population. To solve this problem, this paper proposes a preference-inspired coevolutionary algorithm based on a differentiated allocation strategy (PICEAg-DS). First, it sets up an external archive to save the nondominated solutions and then extracts the convergence and diversity information from it. Second, it divides the objective space into several subspaces and designs a space distance operator to evaluate their optimization difficulty. Finally, it dynamically assigns the target vectors and guides more computational resource to the difficult to optimize subspaces, and thus drives the whole population evolution. To prove the advantages of differentiated resource allocation strategy, the PICEAg-DS is compared with two classic coevolutionary algorithms (PICEAg, CMOPSO) and two classic MOEAs based on resource allocation strategy (EAG-MOEAD, MOEAD-DRA). The experimental results show that PICEAg-DS performs better than the other algorithms on many WFG test problems. To further analysis the effectiveness of PICEAg-DS, compare it with two MOEAs based on domination relationship (NSGAII, SPEA2) and two MOEAs based on decomposition (RVEA, MOEA/D-M2M) on MOP and UF test suite. The experimental results show the PICEAg-DS has a better convergence than the other comparison algorithms, especially on 3-objective MOP6-7 and UF8-9.

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“…To alleviate the above-mentioned drawback, several studies proposed methodologies that give more consideration to sub-problems with higher contributions to the overall objective value [13], [37]- [39]. These methods are known as contribution-based CC (CBCC).…”

Section: B Contribution-based Cooperative Co-evolutionmentioning

confidence: 99%

Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

et al. 2020

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The solution of large-scale optimization problems is the key to many decision-making processes in practice. However, it is a challenging research topic when considered both the quality of solutions and the required computational time. One of the popular approaches for these problems is to divide the problems into a number of smaller sub-problems, that are then solved separately with an exchange of some information using the cooperative co-evolution (CC) concept. However, the characteristics of subcomponents could be different, and their contributions to the overall performance can also be different while solving the problem. In the CC approach, it usually applies one optimizer and allocates equal computational budget to all sub-components. In this paper, a new algorithm is proposed with the use of multiple optimizers, along with a need-based allocation of computational budget for the sub-components. In the proposed algorithm, a group of optimizers cooperate in an effective way to evolve the sub-components, depending on heuristic fuzzy rules. The performance of our proposed algorithm was evaluated by solving a number of large-scale global optimization benchmark functions. The empirical results show that the proposed algorithm outperforms equal allocation CC, a single selection characteristic, a single candidate optimizer and state-of-the-art algorithms. INDEX TERMS Cooperative co-evolution, large-scale optimization, fuzzy logic.

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A new algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution

Cited by 41 publications

References 25 publications

Cooperative co-evolution for feature selection in Big Data with random feature grouping

Cooperative co-evolution for feature selection in Big Data with random feature grouping

Preference-Inspired Coevolutionary Algorithm Based on Differentiated Resource Allocation Strategy

Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

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