Concurrently searching branches in software tests generation through multitask evolution

Sagarna, Ramón; Ong, Yew-Soon

doi:10.1109/ssci.2016.7850040

Cited by 43 publications

(21 citation statements)

References 11 publications

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“…Wen and Ting (2016) adopted the concept of MFEA on genetic programming for building ensemble of decision trees. Gupta Sagarna and Ong (2016) used MFEA to solve the software testing problems. Chandra et al (2016) utilized MFEA to optimize the architecture and parameters of feed forward neural network.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

Liaw

Ting

2019

AAAI

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Evolutionary multitasking is a significant emerging search paradigm that utilizes evolutionary algorithms to concurrently optimize multiple tasks. The multi-factorial evolutionary algorithm renders an effectual realization of evolutionary multitasking on two or three tasks. However, there remains room for improvement on the performance and capability of evolutionary multitasking. Beyond three tasks, this paper proposes a novel framework, called the symbiosis in biocoenosis optimization (SBO), to address evolutionary many-tasking optimization. The SBO leverages the notion of symbiosis in biocoenosis for transferring information and knowledge among different tasks through three major components: 1) transferring information through inter-task individual replacement, 2) measuring symbiosis through intertask paired evaluations, and 3) coordinating the frequency and quantity of transfer based on symbiosis in biocoenosis. The inter-task individual replacement with paired evaluations caters for estimation of symbiosis, while the symbiosis in biocoenosis provides a good estimator of transfer. This study examines the effectiveness and efficiency of the SBO on a suite of many-tasking benchmark problems, designed to deal with 30 tasks simultaneously. The experimental results show that SBO leads to better solutions and faster convergence than the state-of-the-art evolutionary multitasking algorithms. Moreover, the results indicate that SBO is highly capable of identifying the similarity between problems and transferring information appropriately.

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Section: Related Workmentioning

confidence: 99%

Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

Liaw

Ting

2019

AAAI

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“…In the literature, there exist a lot of works to apply MFEA to tackle real-world problems, such as complex supply chain network management [16], bi-level optimization problem [14], double-pole balancing problem [17], composites manufacturing problem [14,18], branch testing in software engineering [19], cloud computing service composition problem [20], pollution-routing problem [21], operational indices optimization of beneficiation process [22], and time series prediction problem [23].…”

Section: Related Work On Mtomentioning

confidence: 99%

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Wang¹,

Xu²,

Rong³

et al. 2019

IJCIS

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A B S T R A C TMulti-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization. MTO paradigm [6]. If the optimization tasks happen to bear some commonality or complementarity, then the inclusion of knowledge transfer often leads to significant performance improvements relative to conventional EAs alone.

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“…5) How to apply to new applications. Sagarna and Ong [21] applied multi-task evolutionary computation to concurrently searching branches in software tests generation. Tang, Gong and Zhang [22] used evolutionary multitasking to evolve the modular topologies of extreme learning machine classifiers.…”

Section: Introductionmentioning

confidence: 99%

Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

Tan

2020

IEEE Trans. Fuzzy Syst.

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Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multitask optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multitask optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel and easy-toimplement multi-tasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single-and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTGA outperformed all of them, and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.

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Concurrently searching branches in software tests generation through multitask evolution

Cited by 43 publications

References 11 publications

Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

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