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Balance between exploitation and exploration is a main factor influencing convergence in an evolutionary algorithm. In order to improve this balance new trends in evolutionary algorithms make use of multi-recombinative a...

The Flow Shop Scheduling Problem have been tackled using different techniques which goes from mathematical techniques like Branch and Bound to metaheuristics like evolutionary algorithms (EAs). Although in the real worl...

The topic of this paper is related to the study of what is known in the industry as the “parent-child” effect. This is accomplished by using a coupled hydraulic fracture and reservoir simulator to perform a sensitivi...

In the restricted single-machine common due date problem, the goal is to find a schedule for the n jobs which jointly minimizes the sum of earliness and tardiness penalties. This problem, even in its simplest formulatio...

Allocation of the components (tasks) of a parallel program to processors in a multiprocessor or a multicomputer system take full advantage of the computational power provided by the system. Evolutionary approaches has b...

Evolutionary algorithms (EAs) can be used as optimisation mechanisms. Based on the model of natural evolution, they work on populations of individuals instead of on single solutions. In this way, the search is performed ...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

The study of earliness and tardiness penalties in scheduling is a relatively recent area of research. In the past, traditionally the emphasis was put on regular measures that are nondecreasing in job completion times suc...

In evolutionary algorithms based on genetics, the crossover operation creates individuals by interchanging genes. On the other side selection mechanisms aim to favour reproduction of better individuals imposing a directi...

Multiobjective optimization, also known as vector-valued criteria or multicriteria optimization, have long been used in many application areas where a problem involves multiple objectives, often conflicting, to be met or...

As a new promising crossover method, multiple crossovers per couple (MCPC) deserves special attention in evolutionary computing field. Allowing multiple crossovers per couple on a selected pair of parents provided an ext...

Jobs to be delivered in a production system are usually weighted according to clients requirements and relevance. Attempting to achieve higher customer satisfaction trends in manufacturing are focussed today on productio...

This work summarizes results when facing the problem of allocating a number of nonidentical tasks in a parallel system. The model assumes that the system consists of a number of identical processors and that only one tas...

In previous works the ability of CPS-MCPC (an evolutionary, co-operative, population search method with multiple crossovers per couple) to build well delineated Pareto fronts in diverse multiobjective optimization proble...

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