On the performance of classification algorithms for learning Pareto-dominance relations

Bandaru, Sunith; Ng, Amos H. C.; Deb, Kalyanmoy

doi:10.1109/cec.2014.6900641

Cited by 34 publications

(9 citation statements)

References 22 publications

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“…In the second category of SAEAs, the surrogate serves as a classifier [53], [48] that divides the candidate solutions into good or bad solutions, e.g., dominated or non-dominated solutions. So far, relatively less work has been published that uses surrogate as a classifier, e.g., the classification and Pareto domination based MOEA (CPS-MOEA) [46] and the MOEA based on decomposition and preselection (MOEA/DP) [54].…”

Section: A Surrogate-assisted Optimizationmentioning

confidence: 99%

A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

Pan

Cheng

Tian

et al. 2019

IEEE Trans. Evol. Computat.

323

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Surrogate-assisted evolutionary algorithms have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing surrogate-assisted evolutionary algorithms are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogateassisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of manyobjective optimization test problems.

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Section: A Surrogate-assisted Optimizationmentioning

confidence: 99%

A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

Pan

Cheng

Tian

et al. 2019

IEEE Trans. Evol. Computat.

323

View full text Add to dashboard Cite

show abstract

“…A similar algorithm to [88] was proposed in [10], where nondominated individuals were also used during classification. In this algorithm, a metamodel is built for three classes while doing pairwise dominance comparison.…”

Section: Problem and Fitness Approximationmentioning

confidence: 99%

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

et al. 2017

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Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008-2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation based algorithms. We also compare these algorithms based on different criteria such as metamodelling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive mmultiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.

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“…By contrary, there are also some SAEAs using the surrogate models for classification to learn the Pareto dominance relationship or the Pareto rankings [83]. In 2014, Bandaru et al trained a multi-class surrogate classifier to determine the dominance relationship between two candidate solutions [84]. In 2015, Bhattacharjee and Ray proposed a support vector machine-based surrogate to learn the ranking of solutions for constrained multiobjective optimization problems [85].…”

Section: Multi-objective Saeasmentioning

confidence: 99%

Model-based evolutionary algorithms: a short survey

Cheng

Jin

et al. 2018

Complex Intell. Syst.

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The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given.

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On the performance of classification algorithms for learning Pareto-dominance relations

Cited by 34 publications

References 22 publications

A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

Model-based evolutionary algorithms: a short survey

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