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In an m-machines n-jobs flow-shop sequencing problem each job consists of m operations and each operation requires a different machine, so n jobs have to be processed in the same sequence on m machines. The processing ti...

Tardiness related objectives are of utmost importance in production systems when client satisfaction is a main goal of a company. These objectives measure the system response to the client requirements and rate manager´...

Sequential and parallel evolutionary algorithms (EAs) are developed and evaluated on two hard optimisation problems arising in the field of Telecommunications: designing error correcting codes, and finding optimal placem...

Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically parallel nature of genetic algorithms. By distributing the total population, these models ref1ects a bebaviour nearer to that...

Improvements in evolutionary algorithms (EAs) consider multirecombination, allowing multiple crossover operations on a pair of parents (MCPC, multiple crossovers per couple) or on a set of multiple parents (MCMP, multipl...

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...

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...

In this work a solution using evolutionary algorithms with penalty function for the non-guillotine cutting problem is presented. In this particular problem, the rectangular pieces have to be cut from an unique large obje...

Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model invol...

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...

Numerical Taxonomy aims to group in clusters, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa) through numerical methods. Clusters that consitute families was the purpose of this...

Rough Sets Theory has opened new trends for the development of the Incomplete Information Theory. Inside this one, the notion of reduct is a very significant one, but to obtain a reduct in a decision system is an expensi...

From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic al...

Multiple crossover per couple (MCPC) is a newly introduced crossover method which in contrast with the single crossover per couple approach (SCPC), permits more than one crossover operation for each mating pair. MCPC was...

Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This mode...

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...

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, while for the weighted tardiness problem the go...

A job shop can be seen as a multi-operation model where jobs follows fixed routes, but not necessarily the same for each job. Job Shop Scheduling (JSS) attempts to provide optimal schedules according to some criterion. C...

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 ...

Ant Colony Optimization algorithms are intrinsically distributed algorithms where independent agents are in charge of building solutions. Stigmergy or indirect communication is the way in which each agent learns from the...

This work presents a systematic literature review on the application of Machine Learning algorithms in the development of effective movie recommender systems. With the increasing popularity of movie recommender systems i...

The paper analyses and compares alternative iterative and recursive implementations of N-ary search algorithms in hardware (in field programmable gate arrays, in particular). The improvements over the previous results ha...

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describe...

OBJECTIVES: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this,...