Parallel Genetic Algorithms: Theory and Real World by Gabriel Luque
By Gabriel Luque
This booklet is the results of numerous years of analysis attempting to larger signify parallel genetic algorithms (pGAs) as a strong software for optimization, seek, and studying. Readers can clear up complicated projects via decreasing their excessive computational occasions. facing medical fields (parallelism and gasoline) is usually tricky, and the booklet seeks at gracefully introducing from simple strategies to complex topics.
The presentation is established in 3 components. the 1st one is focused to the algorithms themselves, discussing their elements, the actual parallelism, and most sensible practices in utilizing and comparing them. A moment half bargains with the idea for pGAs, with an eye fixed on theory-to-practice matters. a last 3rd half bargains a truly extensive examine of pGAs as sensible challenge solvers, addressing domain names equivalent to traditional language processing, circuits layout, scheduling, and genomics.
This quantity should be worthwhile either for researchers and practitioners. the 1st half indicates pGAs to both rookies and mature researchers trying to find a unified view of the 2 fields: fuel and parallelism. the second one half in part solves (and additionally opens) new research traces in conception of pGAs. The 3rd half might be accessed independently for readers drawn to purposes. the result's a superb resource of data at the state-of-the-art and destiny advancements in parallel GAs.
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Additional info for Parallel Genetic Algorithms: Theory and Real World Applications
In the next subsection we present several examples of utilization of the performance measures. We will highlight how wrong selections of metrics aﬀect to the achieved conclusions. 1 Example 1: On the Absence of Information We begin our test showing the results of a SA with diﬀerent number of processors to solve an instance of MAXSAT. 2. The values showed are the number of executions that found the optimal value (% hit column), the ﬁtness of the best solution (best column), the average ﬁtness (avg column), the number of evaluations (# evals column) and the running time (time column).
This class of instances has the advantage of being freely available. Specially, academic instances must be analyzed in the existing literature to not reinvent the wheel, and to avoid using straightforward benchmarks . Standard Instances In this class are included the instances, benchmarks, and problem instance generators that, due to their wide use in experimentation, became standard in the specialized literature. For example, Reinelt  oﬀers the TSPLIB, a travelling salesman problem test instances, Demirkol et al.
At the beginning of each iteration, all the processors send the individuals of their ﬁrst/last column/row to their neighbor islands (see Fig. 6). After receiving the individuals from the neighbors, a sequential cGA is executed in each subpopulation. The remaining of the algorithms has a canonical implementation and no especial issues are used. For testing the parallel algorithms we have used the well-known MAXSAT problem. The next subsection brieﬂy describes the MAXSAT problem and then we discuss the results.