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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Evolutionary Algorithms
Evolutionary algorithms (EAs) are population-based metaheuristics, originally
inspired by aspects of natural evolution. Modern varieties incorporate a broad
mixture of search mechanisms, and tend to blend inspiration from nature with
pragmatic engineering concerns; however, all EAs essentially operate by
maintaining a population of potential solutions and in some way artificially
'evolving' that population over time. Particularly well-known categories of EAs
include genetic algorithms (GAs), Genetic Programming (GP), and Evolution
Strategies (ES). EAs have proven very successful in practical applications,
particularly those requiring solutions to combinatorial problems. EAs are
highly flexible and can be configured to address any optimization task, without
the requirements for reformulation and/or simplification that would be needed
for other techniques. However, this flexibility goes hand in hand with a cost:
the tailoring of an EA's configuration and parameters, so as to provide robust
performance for a given class of tasks, is often a complex and time-consuming
process. This tailoring process is one of the many ongoing research areas
associated with EAs.Comment: To appear in R. Marti, P. Pardalos, and M. Resende, eds., Handbook of
Heuristics, Springe
An Improved Robot Path Planning Algorithm
Robot path planning is a NP problem. Traditionaloptimization methods are not very effective to solve it. Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improvethe convergence speed to form a new genetic algorithm. Through the series of numerical experiments, the new algorithm has been proved to be efficiency.We also use the proposed algorithm to solve the robot path planning problem and the experiment results indicated that the new algorithm is efficiency for solving the robot path planning problems and the best path usually can be found
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