Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization

Bali, Kavitesh K.; Chandra, Rohitash

doi:10.1007/978-3-319-26555-1_15

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…While islands are evolved separately, they interact with each other through periodical migration (Bali and Chandra 2015 ; Keerativuttitumrong et al 2002 ; Wassermann and Su 2007 ), which is a genetic operator specific to cooperative co-evolutionary algorithms. It consists of copying and injecting the strongest solution from one of the islands to the other ones with the goal of increasing genetic diversity and supporting islands with poor performance (Bali and Chandra 2015 ) (e.g., no improvements in local fitness scores). During migration, the island with the largest fitness improvements in local fitness score is selected and the strongest solution from that island is copied into the other islands.…”

Section: Introductionmentioning

confidence: 99%

Search-based multi-vulnerability testing of XML injections in web applications

et al. 2019

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Modern web applications often interact with internal web services, which are not directly accessible to users. However, malicious user inputs can be used to exploit security vulnerabilities in web services through the application front-ends. Therefore, testing techniques have been proposed to reveal security flaws in the interactions with back-end web services, e.g., XML Injections (XMLi). Given a potentially malicious message between a web application and web services, search-based techniques have been used to find input data to mislead the web application into sending such a message, possibly compromising the target web service. However, state-of-the-art techniques focus on (search for) one single malicious message at a time. Since, in practice, there can be many different kinds of malicious messages, with only a few of them which can possibly be generated by a given front-end, searching for one single message at a time is ineffective and may not scale. To overcome these limitations, we propose a novel co-evolutionary algorithm (COMIX) that is tailored to our problem and uncover multiple vulnerabilities at the same time. Our experiments show that COMIX outperforms a single-target search approach for XMLi and other multi-target search algorithms originally defined for white-box unit testing.

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Section: Introductionmentioning

confidence: 99%

Search-based multi-vulnerability testing of XML injections in web applications

et al. 2019

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“…In this section, we analyze the performance of MIC 4 in terms of function evaluations and solution quality. Table V contains the experimental results for comparing the performance of the proposed MIC 4 algorithm against MIC 3 [22]. We first evaluate the performance of MIC 4 with the Kill strategy against MIC 3 .…”

Section: B Resultsmentioning

confidence: 99%

“…Competitive island cooperative coevolution (CICC) [21,38] and multi-island competitive cooperative coevolution (MIC 3 ) [22,23] were proposed for solving global optimization problems. In CICC, two different problem decompositions are implemented as islands that compete and collaborate to solve a problem.…”

Section: B Multi-island Competitive Cooperative Coevolutionmentioning

confidence: 99%

“…Competition and collaboration features have shown to be advantageous in cooperative coevolution [20,21]. A multi-island competitive cooperative coevolution method (MIC 3 ) was introduced in which various problem decomposition strategies were implemented as islands that compete and collaborate to optimize a problem [22]. Experimental results demonstrated that MIC 3 outperforms standalone traditional CC and converges to high quality solutions without having the need to find an optimal decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Contribution based multi-island competitive cooperative coevolution

Bali

Chandra²,

Omidvar

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Competition in Cooperative Coevolution (CC) has demonstrated success in solving global optimization problems. In a recent study, a multi-island competitive cooperative coevolution (MIC 3) algorithm was introduced, which featured competition and collaboration of several different problem decomposition strategies implemented as independent islands. It was shown that MIC 3 converges to high quality solutions without the need to find an optimal decomposition. MIC 3 splits the computational budget in terms of number of function evaluations equally amongst all the islands and evolves them in a round-robin fashion. This overlooks the difference in contributions of the different islands towards improving the overall objective function value. Therefore, a considerable amount of function evaluations are wasted on the low-contributing islands as their problem decomposition strategies may not appeal to the problem at the given stage of the evolutionary process. This paper proposes Contribution Based MIC 3 algorithms (MIC 4) that quantifies the contributions of each island and allocates the computational budget accordingly. Experimental analysis reveals that MIC 4 outperforms MIC 3 .

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“…Cooperative coevolution has been prominent for global optimization problems [49] and neuroevolution [50,51]. There have been a number of extensions of the cooperative coevolution framework [52], including the multi-island competitive cooperative coevolution (MICCC) [53], which used a real-coded genetic algorithm in the subpopulations. Bali and Chandra [54] demonstrated significant improvements in MICC when compared to earlier counterparts, and hence we use it to further compare our results for Setting 2 (D = 100).…”

Section: Further Comparisonsmentioning

confidence: 99%

A Quantum-Inspired Predator–Prey Algorithm for Real-Parameter Optimization

Khan,

Hussain,

Chandra

2024

Algorithms

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Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In this paper, we present the quantum predator–prey algorithm (QPPA), which fuses the fundamentals of quantum computing and swarm optimization based on a predator–prey algorithm. Our results demonstrate the efficacy of QPPA in solving complex real-parameter optimization problems with better accuracy when compared to related algorithms in the literature. QPPA achieves highly rapid convergence for relatively low- and high-dimensional optimization problems and outperforms selected traditional and advanced algorithms. This motivates the application of QPPA to real-world application problems.

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Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization

Cited by 5 publications

References 16 publications

Search-based multi-vulnerability testing of XML injections in web applications

Search-based multi-vulnerability testing of XML injections in web applications

Contribution based multi-island competitive cooperative coevolution

A Quantum-Inspired Predator–Prey Algorithm for Real-Parameter Optimization

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