A novel genetic algorithm called GGA (Grid-based Genetic Algorithm) is presented to improve the optimization of multimodal real functions. The search space is discretized using a grid, making the search process more efficient and faster. An integer-real vector codes the genotype and a GA is used for evolving the population. The integer part allows us to explore the search space and the real par...

yard crane is an important resource in container terminals. efficient utilization of the yard crane significantly improves the productivity and the profitability of the container terminal. this paper presents a mixed integer programming model for the yard crane scheduling problem with non- interference constraint that is nphard in nature. in other words, one of the most important constraints in...

Hybrid algorithms formed by the combination of Genetic Algorithms with Local Search methods provide increased performances when compared to real coded GA or Local Search alone. However, the cost of Local Search can be rather high. In this paper we present a new hybrid algorithm which reduces the total cost of local search by avoiding the start of the method in basins of attraction where a local...

This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749–768; T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber rep...

optimization of cutting conditions is a non-linear optimization with constraint and it is very important to the increase of productivity and the reduction of costs. in recent years, several evolutionary and meta-heuristic optimization algorithms were introduced. the cuckoo optimization algorithm (coa) is one of several recent and powerful meta-heuristics which is inspired by the cuckoos and the...

This paper analyzes the multicriteria bimatrix goal game under the light of entropy environment. In this approach, the entropy functions of the players are considered as objectives to the bimatrix game. The solution concepts behind this game are based on getting the probability to achieve some specified goals by determining G-goal security strategies (GGSS). We define the real coded Genetic Alg...

This paper proposes a new real-value mutation operator and a hybrid real-coded genetic algorithm with quasi-simplex technique using this new mutation operator (RCGAQS). Compared with the classical GA (CGA), RCGAQS has the following distinguish features: (1) A new real-value mutation mechanism was used to increase the capability of global search (exploration); (2) The modified simplex technique,...

In this paper, a multi-objective genetic agorithm to solve assembly line balancing problems is proposed. The performance criteria considered are the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index after trade and transfer. The developed genetic algorithm is compared with six popular heuristic algorithms, namely, ranked positi...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrat...