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In this paper, we consider a Prize Colleting Vehicle Routing Problem (PCVRP). Prize collecting Vehicle Routing Problem is a Variant of Vehicle routing Problem. One of the major concerns of the Prize collecting Vehicle routing problem is that all customers need not to be visited compulsory, but prize must be collected from each customer visited and a penalty has to be paid for every unvisited customer...
This research treats the stock turning point prediction as the imbalanced data classification problems and proposes the evolving weighted support vector machines (EW-SVM) system that leads to superior predictions upon the direction-of-change of the market. However, many parameters of the w-SVM model have to be decided by the user beforehand. Therefore, the EW-SVM system combining both w-SVM with GA...
This research presents a two-stage AIS approach to solve the Grid scheduling problems. According to the literature survey, most researchers use the clone selection of B cells during the evolving processes and the function of B cells in AIS researches to solve various optimization problems. Instead, we try to implement the T helper cell and T suppressor cell in T cell combining B cell to solve the...
In this paper, a Hybrid Genetic-Immune algorithm (HGIA) is developed to solve the flow-shop scheduling problems. The regular genetic algorithm is applied in the first-stage to rapidly evolve and when the processes are converged up to a pre-defined iteration then the Artificial Immune System (AIS) is introduced to hybridize Genetic Algorithm (GA) in the second stage. Therefore, HGIA continues to search...
In this paper, a bionic algorithm based on Genetic Algorithms is proposed as a varietal GA, named External Self-evolving Multiple-archives (ESMA). ESMA focuses on improving the efficiency of applying diversity for enhancing the solution quality. This paper proposes three mechanisms for self-evolving Multiple-archives, which are Clustering Strategy, Switchable Mutation and Elitist Propagation. These...
This paper proposed self-guided genetic algorithm, which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM), to solve strong NP-hard flowshop scheduling problems with the minimization of makespan. Most EAPM research explicitly used the probabilistic model from the parental distribution, then generated solutions by sampling from the probabilistic...
Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, evolving neural network model with dynamic time warping piecewise linear representation system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base,...
With the increase in manufacturing complexity, conventional production scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. Therefore, applying efficient algorithm to solve the scheduling problems is essential for reducing the time budget. Genetic algorithms (GAs) is very effective in solving discrete combinatorial problems but they are frequently faced...
In this study we integrate a self-guided genetic algorithm with dominance properties (DPs) which is named DP-Self-guided GA. Self-guided GA is belonged to the category of evolutionary algorithms based on probabilistic models (EAPM) and it is effective and efficient in solving the scheduling problems. In order to further enhance the performance of this algorithm, it is thus integrated with DPs because...
Premature convergence in the process of genetic algorithm (GA) for searching solution is frequently faced and the evolutionary processes are often trapped in a local but not global optimum. This phenomenon occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature,...
The applications of genetic algorithms (GAs) in solving combinatorial problems are frequently faced with a problem of early convergence and the evolutionary processes are often trapped in a local but not global optimum. This premature convergence occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance...
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