Model-based evolutionary algorithms: a short survey

Many real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

Section: Literature Surveymentioning

confidence: 99%

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Shaw

Ahmed

Malakar³

et al. 2021

“…Moreover, a solution for {θ i } (∀i), which can approximately satisfy the condition, should be found. Optimization techniques such as Genetic Algorithms (GAs) have great potential in solving this sort of problem [7,17,23].…”

Section: Reconfigure In Accordance With the Shape Of An Obstaclementioning

confidence: 99%

Toward complete area coverage of a reconfigurable tiling robot by following obstacle shape

Samarakoon

Muthugala

et al. 2021

Complete area coverage is a crucial factor for a floor cleaning robot. Self-reconfigurable tiling robots have been introduced over robots with a fixed shape for floor cleaning since they improve the area coverage by the flexibility of shape-shifting in cluttered environments. The existing coverage methods of reconfigurable tiling robots follow the tiling theory to cope with the area coverage problem. However, these methods merely consider a limited set of predefined shapes for the reconfiguration of a robot. The consideration of a limited set of predefined shapes for the reconfiguration impedes the ability of coverage to a certain extent in typical floor environments. Therefore, this paper proposes a novel method to improve area coverage of a tiling robot by reconfiguring according to the shape of obstacles. To this end, the required hinge angles for reconfiguring per the shape of an obstacle are determined by a genetic algorithm. The proposed method considers an optimized shape for reconfiguration in lieu of a limited set of predefined shapes. The coverage improvement of the proposed concept has been compared against the existing coverage methods of tiling robots to validate the performance. According to the experimental results, the proposed method surpasses the existing coverage methods of tiling robots from the perspective of area coverage, and the improvement is significant and noteworthy.

“…The evaluation of some practical engineering problems such as antenna design [16], blast optimization [17], trauma system design [18], and power system design [19] are time-consuming and costly so that the expense of obtaining the optimal solution is unaffordable. Therefore, as an efficient tool for expensive optimization problems, the surrogate model [20,21] has attracted much attention from researchers in different fields. In the expensive optimization, the evaluation of objectives and constraints can only be performed through surrogates trained by data collected from physical experiments, numerical simulations, or daily life.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Wang

Xiong

et al. 2021

Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.