1,665 research outputs found
Competitive island - based cooperative coevolution for efficient optimization of large - scale fully - separable continuous functions
In this paper, we investigate the performance of introducing competition in cooperative coevolutionary algorithms to solve large-scale fully-separable continuous optimization problems. It may seem that solving large-scale fully-separable functions is trivial by means of problem decomposition. In principle, due to lack of variable interaction in fully-separable problems, any decomposition is viable. However, the decomposition strategy has shown to have a significant impact on the performance of cooperative coevolution on such functions. Finding an optimal decomposition strategy for solving fully-separable functions is laborious and requires extensive empirical studies. In this paper, we use a competitive two-island cooperative coevolution in which two decomposition strategies compete and collaborate to solve a fully-separable problem. Each problem decomposition has features that may be beneficial at different stages of optimization. Therefore, competition and collaboration of such decomposition strategies may eliminate the need for finding an optimal decomposition. The experimental results in this paper suggest that com-
petition and collaboration of suboptimal decomposition strategies of a fully-separable problem can generate better solutions than the standard cooperative coevolution with standalone decomposition strategies. We
also show that a decomposition strategy that implements competition against itself can also improve the overall optimization performance
Cooperative co-evolution with differential grouping for large scale optimization
Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems. However, in practice, without prior knowledge about the problem, it is not clear how the problem should be decomposed. In this paper, we propose an automatic decomposition strategy called differential grouping that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum. We show mathematically how such a decomposition strategy can be derived from a definition of partial separability. The empirical studies show that such near-optimal decomposition can greatly improve the solution quality on large-scale global optimization problems. Finally, we show how such an automated decomposition allows for a better approximation of the contribution of various subcomponents, leading to a more efficient assignment of the computational budget to various subcomponents
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of
features. When artificial neural networks (ANNs) are used for modeling these
tasks, the network architectures tend to be large. Neuroevolution is an
effective approach for optimizing ANNs; however, there are two bottlenecks that
make their application challenging in case of high-dimensional networks using
direct encoding. First, classic evolutionary algorithms tend not to scale well
for searching large parameter spaces; second, the network evaluation over a
large number of training instances is in general time-consuming. In this work,
we propose an approach called the Limited Evaluation Cooperative
Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize
high-dimensional ANNs.
The proposed method aims to optimize the pre-synaptic weights of each
post-synaptic neuron in different subpopulations using a Cooperative
Co-evolutionary Differential Evolution algorithm, and employs a limited
evaluation scheme where fitness evaluation is performed on a relatively small
number of training instances based on fitness inheritance. We test LECCDE on
three datasets with various sizes, and our results show that cooperative
co-evolution significantly improves the test error comparing to standard
Differential Evolution, while the limited evaluation scheme facilitates a
significant reduction in computing time
Multi - island competitive cooperative coevolution for real parameter global optimization
Problem decomposition is an important attribute of cooperative coevolution that depends on the nature of the problems in terms of separability which is defined by the level of interaction amongst decision variables. Recent work in cooperative coevolution featured competition and collaboration of problem decomposition methods that was implemented as islands in a method known as competitive island cooperative coevolution (CICC). In this paper, a multi-island competitive cooperative coevolution algorithm (MICCC) is proposed in which several different problem decomposition strategies are given a chance to compete,
collaborate and motivate other islands while converging to a common solution.
The performance of MICCC is evaluated on eight different benchmark functions
and are compared with CICC where only two islands were utilized. The results from the experimental analysis show
that competition and collaboration of several different island can yield solutions with a quality better than
the two-island competition algorithm (CICC) on most
complex multi-modal problems
High-dimensional Black-box Optimization via Divide and Approximate Conquer
Divide and Conquer (DC) is conceptually well suited to high-dimensional
optimization by decomposing a problem into multiple small-scale sub-problems.
However, appealing performance can be seldom observed when the sub-problems are
interdependent. This paper suggests that the major difficulty of tackling
interdependent sub-problems lies in the precise evaluation of a partial
solution (to a sub-problem), which can be overwhelmingly costly and thus makes
sub-problems non-trivial to conquer. Thus, we propose an approximation
approach, named Divide and Approximate Conquer (DAC), which reduces the cost of
partial solution evaluation from exponential time to polynomial time.
Meanwhile, the convergence to the global optimum (of the original problem) is
still guaranteed. The effectiveness of DAC is demonstrated empirically on two
sets of non-separable high-dimensional problems.Comment: 7 pages, 2 figures, conferenc
Cooperative co-evolution for feature selection in big data with random feature grouping
© 2020, The Author(s). 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
Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization
Cooperative co-evolution (CC) is an explicit means of problem decomposition in multipopulation evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents of a large-scale optimization problem co-evolve, and are likely to have different contributions to the improvement of the best overall solution to the problem. Hence, it makes sense that more computational resources should be allocated to the subpopulations with greater contributions. In this paper, we study how to allocate computational resources in this context and subsequently propose a new CC framework named CCFR to efficiently allocate computational resources among the subpopulations according to their dynamic contributions to the improvement of the objective value of the best overall solution. Our experimental results suggest that CCFR can make efficient use of computational resources and is a highly competitive CCFR for solving large-scale optimization problems
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