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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 ...

Evolutionary Computation is an emergent field, which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. Exploration and exploitation of solution i...

Over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully ...

The travelling salesman problem (TSP) is a NP-hard problem. Techniques as either Branch and Bound or Dynamic Programming supplied the global optimum solution for instances with more than 7000 cities. But, ther needed mor...

Scheduling is an active area of research in applied artificial intelligence. Scheduling problems typically comprise several concurrent (and often conflicting) goals, and several resources which may be allocated in order ...

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 scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling...

A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months fr...

Different evolutionary approaches using genetic algorithms were proposed to solve the Flow Shop Scheduling Problem (FSSP). Variants point to the selection mechanism, genetic operators and the decision to include or not i...

We present an evolutionary algorithm that evolves a population of local approximators in order to fit an unknown function. The evolutionary algorithm performs a simultaneous learning of simple local approximators togethe...

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...

UNLABELLED: We have developed a new software package, Molecular Evolutionary Genetics Analysis version 2 (MEGA2), for exploring and analyzing aligned DNA or protein sequences from an evolutionary perspective. MEGA2 vastl...

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available ...