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Highlights • Genetic algorithms (GAs) are used to find optimal assignment of tasks to teams of collaborating agents, where performance of a team is based on the collaboration of its members. • We show the shortcoming of standard crossover operators to efficiently solve large instances of the problem. • We suggest extension (shuffled list) to standard crossover operators and introduce two more operators (team-based and team-based shuffled list), which all are capable to efficiently solve the problem. • The position-based crossover operator with a shuffled list is singled out to have the best performance. • Performance of the GAs is further enhanced by using chaotic sequences and the results are significant as compared with PSO and DE. Abstract The problem of optimally assigning agents (resources) to a given set of tasks is known as the assignment problem (AP). The classical AP and many of its variations have been extensively discussed in the literature. In this paper, we examine a specific class of the problem, in which each task is assigned to a group of collaborating agents. APs in this class cannot be solved using the Hungarian or other known polynomial time algorithms. We employ the genetic algorithm (GA) to solve the problem. However, we show that if the size of the problem is large, then standard crossover operators cannot efficiently find near-optimal solutions within a reasonable time. In general, the efficiency of the GA depends on the choice of genetic operators (selection, crossover, and mutation) and the associated parameters. In order to design an efficient GA for determining the near-optimal assignment of tasks to collaborative agents, we focus on the construction of crossover operators. We analyze why a naive implementation with standard crossover operators is not capable of sufficiently solving the problem. Furthermore, we suggest modifications to these operators by adding a shuffled list and introduce two new operators (team-based and team-based shuffled list). We demonstrate that the modified and new operators with shuffled lists perform significantly better than all operators without shuffled lists and solve the presented AP more efficiently. The performance of the GA can be further enhanced by using chaotic sequences. Moreover, the GA is also compared with the particle swarm optimization (PSO) and differential evolution (DE) algorithms, demonstrating the superiority of the GA over these search algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method to solve disassembly sequence planning problem. Firstly, disassembly feasibility information graph (DFIG) is presented to describe the product disassembly sequence and operation information. Then, disassembly sequence planning problem is mapped onto the DFIG as an optimal path-searching problem. Based on this idea, a genetic algorithm is provided to find out feasible and optimal disassembly solutions efficiently. Finally, a case study, which is used to illustrate the performance of proposed method, is presented. [Copyright &y& Elsevier]

Abstract: Gene prediction is still an important step to annotate genomes. In this paper, we proposed a novel method for recognizing gene in genomes. The method combines three famous gene-finding programs. After calculating the accuracy parameters, the equitable weight for each parameter is calculated using genetic algorithm. Then the integrative evaluation is performed. The integrative evaluation is employed to instruct the training of an artificial neural network. The simulation results show that the proposed method integrates advantages of three programs and the accuracy has an obvious improvement, which indicate that the proposed method has a powerful capability for gene prediction. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a massively parallel architecture for hardware implementation of genetic algorithms. This design is quite innovative as it provides a viable solution to the fitness computation problem, which depends heavily on the problem-specific knowledge. The proposed architecture is completely independent of such specifics. It implements the fitness computation using a neural network. The hardware implementation of the used neural network is stochastic and thus minimise the required hardware area without much increase in response time. Last but not least, we demonstrate the characteristics of the proposed hardware and compare it to existing ones. [Copyright &y& Elsevier]

Abstract: The classification ability of a single-layer perceptron could be improved by considering some enhanced features. In particular, this form of neural networks is called a functional-link net. In the output neuron''s activation function, such as the sigmoid function, an inner product of a connection weight vector with an input vector is computed. However, since the input features are not independent of each other for the enhanced pattern, an assumption of the additivity is not reasonable. This paper employs a non-additive technique, namely the fuzzy integral, to aggregate performance values for an input pattern by interpreting each of the connection weights as a fuzzy measure of the corresponding feature. A learning algorithm with the genetic algorithm is then designed to automatically find connection weights. The sample data for bankruptcy analysis obtained from Moody''s Industrial Manuals is considered to examine the classification ability of the proposed method. The results demonstrate that the proposed method performs well in comparison with traditional functional-link net and multivariate techniques. [Copyright &y& Elsevier]