Hierarchical genetic algorithm with new evaluation function and bi-coded representation for the selection of features considering their confidence rate

Hamdani, Tarek M.; Won, Jin-Myung; Alimi, Adel M.; Karray, Fakhri

doi:10.1016/j.asoc.2010.08.020

Cited by 39 publications

(17 citation statements)

References 10 publications

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“…Take seven containers, three stacks, and three tiers, for instance. In Table 1, the first line represents the number of incoming containers, the second line a solution = [1,2,3,3,2,1,3], and the third line the value of particles randomly generated in the range [1,3]. The solution indicates that the first and the sixth containers are allocated to stack 1, the second and the fifth containers are allocated to stack 2, and the third, fourth, and seventh containers are allocated to stack 3.…”

Section: Encoding Representation and Initial Solutionmentioning

confidence: 99%

“…In this article, initial solution is generated randomly. However, there exists a series of infeasible solution, such as = [1,2,2,3,2,1,2], where the total number of containers assigned to stack 2 exceeds 3. Infeasible solution will reduce the quality of population, so repairing strategy [48] is adopted to improve initial solution to obtain the high-quality initial solution.…”

Section: Encoding Representation and Initial Solutionmentioning

confidence: 99%

“…Thereby, metaheuristic algorithms are applied to find the feasible solutions in a reasonable time. As a classical metaheuristic algorithm, PSO is similar to other evolutionary algorithms such as genetic algorithm (GA) [2,3], ant colony optimization algorithm (ACO) [4], firefly algorithm (FA) [5], and artificial fish swarm algorithm (AFSA) [6]. The common characteristic of above-mentioned algorithms is to simulate a certain feature of a natural creature to search randomly through the solution space.…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Ai-hu

et al. 2019

Mathematical Problems in Engineering

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Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

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Section: Encoding Representation and Initial Solutionmentioning

confidence: 99%

Section: Encoding Representation and Initial Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Ai-hu

et al. 2019

Mathematical Problems in Engineering

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“…Meta-heuristic algorithms dig the search space by keeping the good solutions and improving them (exploitation), as well as looking for the new ones in other areas through the search space (exploration). Examples of Evolutionary Algorithms are the Genetic Algorithm (GA) [ 15 , 16 ], Ant Colony Optimization (ACO) [ 17 , 18 ], Particle Swarm Optimization (PSO) [ 19 , 20 , 21 ], Multi-Objective Evolutionary Algorithms [ 22 , 23 , 24 ] and Bat Algorithms [ 25 , 26 ]. The use of these approaches is still in infancy stages with a debate on which approach is better than the others.…”

Section: Introductionmentioning

confidence: 99%

MOTiFS: Monte Carlo Tree Search Based Feature Selection

Chaudhry

Lee

2018

Entropy

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Given the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find the optimal feature subset through the feature space. The algorithm searches for the best feature subset by combining the benefits of tree search with random sampling. Starting from an empty node, the tree is incrementally built by adding nodes representing the inclusion or exclusion of the features in the feature space. Every iteration leads to a feature subset following the tree and default policies. The accuracy of the classifier on the feature subset is used as the reward and propagated backwards to update the tree. Finally, the subset with the highest reward is chosen as the best feature subset. The efficiency and effectiveness of the proposed method is validated by experimenting on many benchmark datasets. The results are also compared with significant methods in the literature, which demonstrates the superiority of the proposed method.

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“…Fazendo uma analogia com a natureza, não ocorre evolução sem diversidade, poisé necessário que os cromossomos tenham diferentes características genéticas e, consequentemente, diferentes graus de aptidão, para que possa ocorrer seleção natural.3.2.3 Medida de AptidãoA medida de aptidão indica o quão bem adaptado está cada cromossomo da população ao ambiente. Ao longo dos estudos sobre GAs, pesquisas têm mostrado que a especificação de uma medida de aptidão apropriadaé crucial para o desempenho das aplicações.É essencial que a medida de aptidão seja bastante representativa, e diferencie na proporção correta, as soluções promissoras das menos promissoras (ou inadequadas)[45,97,104]. Se houver pouca precisão na avaliação, soluções promissoras podem ser perdidas durante a execução do GA, que gastará mais tempo explorando soluções pouco promissoras, ou pior, pode ser encontrada uma solução de pouca qualidade.…”

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Seleção de características por meio de algoritmos genéticos para aprimoramento de rankings e de modelos de classificação

Silva¹

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Sistemas de recuperação de imagens por conteúdo (Content-based image retrieval-CBIR) e de classificação dependem fortemente de vetores de características que são extraídos das imagens considerando critérios visuais específicos.É comum que o tamanho dos vetores de características seja da ordem de centenas de elementos. Conforme se aumenta o tamanho (dimensionalidade) do vetor de características, também se aumentam os graus de irrelevâncias e redundâncias, levando ao problema da "maldição da dimensionalidade". Desse modo, a seleção das características relevantesé um passo primordial para o bom funcionamento de sistemas CBIR e de classificação. Nesta tese são apresentados novos métodos de seleção de características baseados em algoritmos genéticos (do inglês genetic algorithms-GA), visando o aprimoramento de consultas por similaridade e modelos de classificação. A família Fc ("Fitness coach") de funções de avaliação proposta vale-se de funções de avaliação de ranking, para desenvolver uma nova abordagem de seleção de características baseada em GA que visa aprimorar a acurácia de sistemas CBIR. A habilidade de busca de GA considerando os critérios de avaliação propostos (família Fc) trouxe uma melhora de precisão de consultas por similaridade de até 22% quando comparado com métodos wrapper tradicionais para seleção de características baseados em decision-trees (C4.5), naive bayes, support vector machine, 1-nearest neighbor e mineração de regras de associação. Outras contribuições desta tese são dois métodos de seleção de características baseados em filtragem, com aplicações em classificação de imagens, que utilizam o cálculo supervisionado da estatística de silhueta simplificada como função de avaliação: o silhouette-based greedy search (SiGS) e o silhouette-based genetic algorithm search (SiGAS). Os métodos propostos superaram os métodos concorrentes na literatura (CFS, FCBF, ReliefF, entre outros).É importante também ressaltar que o ganho em acurácia obtido pela família Fc, e pelos métodos SiGS e SiGAS propostos proporcionam também um decréscimo significativo no tamanho do vetor de características, de até 90%.

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Hierarchical genetic algorithm with new evaluation function and bi-coded representation for the selection of features considering their confidence rate

Cited by 39 publications

References 10 publications

Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

MOTiFS: Monte Carlo Tree Search Based Feature Selection

Seleção de características por meio de algoritmos genéticos para aprimoramento de rankings e de modelos de classificação

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