Reference sharing: a new collaboration model for cooperative coevolution

Shi, Min; Gao, Shang

doi:10.1007/s10732-016-9322-9

Cited by 18 publications

(15 citation statements)

References 44 publications

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“…Among the papers reviewed, the majority of the papers focused on FS and IS together; only a couple of papers addressed the FS problem. The performance of the CCEA mostly depends on the decomposition methods, optimizers, and collaboration techniques [50]. Hence, CCEA based optimizations are still unexplored in many areas and need to be investigated.…”

Section: Number Of Objectivesmentioning

confidence: 99%

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

et al. 2020

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The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS.

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Section: Number Of Objectivesmentioning

confidence: 99%

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

et al. 2020

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“…A decomposition strategy is used to decompose a complex problem into several sub-problems based on the structure of the problem (i.e., separable or non-separable problem) with appropriate granularity (Shi & Gao, 2017). The decomposition strategies are classified as static (decomposes a problem before the evolutionary process starts and decomposed sub-problems are fixed (Bucci & Pollack, 2005)) or dynamic (decomposes a problem at the beginning, but sub-problems have the ability to self-adaptively tune to proper collaboration levels at the time of evolutionary process (…”

Section: Problem Decompositionmentioning

confidence: 99%

“…Once the decomposition is performed, each sub-problem is assigned to a population and an evolutionary optimizer (the same or different) is used to evolve them. Evolutionary processes (initialization, fitness evaluation, selection, recombination, mutation, and survivor selection) are performed by populations independently (Shi & Gao, 2017). Sub-problems are evolved sequentially (only one population performs the evolutionary process per generation, while other populations are frozen (Potter, 1997)) or in parallel (all populations perform the evolutionary processes per generation concurrently (Wiegand, 2004).…”

Section: Sub-problems Evolutionmentioning

confidence: 99%

“…The performance of the collaboration is the fitness value to the individual. At the collaboration step, a population of the complete solution is formed by combining the collaborators to each individual of the current population and at the end of a CCEA process, the final solution to the problem is built by combining the individuals with the best collaboration (Shi & Gao, 2017). A number of collaboration strategies have been studied in the literature, including less greedy strategy (Potter, 1997), 1+1 collaboration model (Potter & de Jong, 2000), blended population algorithm (Sofge, De Jong, & Schultz, 2002), 1+N collaboration model (Bucci & Pollack, 2005), archive-based collaboration (Panait, Luke, & Harrison, 2006), N+N collaboration (Hoverstad, 2007), and Reference Sharing (RS) (Shi & Gao, 2017), all of which are significant collaboration models.…”

Section: Collaboration and Evaluationmentioning

confidence: 99%

“…and de Jong(1994) to solve the large-scale complex optimization problems(Rentsen, Zhou, & Teo, 2016) through a divide-and-conquer strategy and by evolving the interacting co-adapted sub-problems. The cooperative co-evolution achieves the promising performance in optimizing many real-world problems, such as function optimization(Potter & de Jong, 1994), designing artificial neural networks(Potter & de Jong, 1995), occurrence of Red Queen dynamics(Pagie & Hogeweg, 2000), and machine Source: Developed by the authors based onShi and Gao (2017).…”

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confidence: 99%

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Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives

Rashid

Choudhury

2019

Problems and Perspectives in Management

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The term “big data” characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs – volume, velocity, variety, and veracity - to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-to-use distributed, scalable, and fault-tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-the-art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions.

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A cooperative coevolutionary genetic programming hyper-heuristic for multi-objective makespan and cost optimization in cloud workflow scheduling

Zaki,

Zeiträg,

Neves

et al. 2024

Computers & Operations Research

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Reference sharing: a new collaboration model for cooperative coevolution

Cited by 18 publications

References 44 publications

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives

A cooperative coevolutionary genetic programming hyper-heuristic for multi-objective makespan and cost optimization in cloud workflow scheduling

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