40,656 research outputs found
Integrating Evolutionary Computation with Neural Networks
There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique
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A genetic algorithm for power distribution system planning
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The planning of distribution systems consists in determining the optimum site and
size of new substations and feeders in order to satisfy the future power demand with
minimum investment and operational costs and an acceptable level of reliability. This
problem is a combinatorial, non-linear and constrained optimization problem. Several
solution methods based on genetic algorithms have been reported in the literature;
however, some of these methods have been reported with applications to small
systems while others have long solution time. In addition, the vast majority of the
developed methods handle planning problems simplifying them as single-objective
problems but, there are some planning aspects that can not be combined into a single
scalar objective; therefore, they require to be treated separately. The cause of these
shortcomings is the poor representation of the potential solutions and their genetic
operators
This thesis presents the design of a genetic algorithm using a direct representation
technique and specialized genetic operators for power distribution system expansion
planning problems. These operators effectively preserve and exploit critical
configurations that contribute to the optimization of the objective function. The
constraints of the problems are efficiently handle with new strategies.
The genetic algorithm was tested on several theoretical and real large-scale power
distribution systems. Problems of network reconfiguration for loss reduction were
also included in order to show the potential of the algorithm to resolve operational
problems. Both single-objective and multi-objective formulations were considered in
the tests. The results were compared with results from other heuristic methods such as
ant colony system algorithms, evolutionary programming, differential evolution and
other genetic algorithms reported in the literature. From these comparisons it was
concluded that the proposed genetic algorithm is suitable to resolve problems of largescale
power distribution system planning. Moreover, the algorithm proved to be
effective, efficient and robust with better performance than other previous methods.National Council for Science and Technology, Mexic
A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems
Traditional Linear Genetic Programming (LGP) algorithms are based only on the
selection mechanism to guide the search. Genetic operators combine or mutate
random portions of the individuals, without knowing if the result will lead to
a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP)
methods were proposed to overcome this issue through a probability model that
captures the structure of the fit individuals and use it to sample new
individuals. This work proposes the use of LGP with a Stochastic Context-Free
Grammar (SCFG), that has a probability distribution that is updated according
to selected individuals. We proposed a method for adapting the grammar into the
linear representation of LGP. Tests performed with the proposed probabilistic
method, and with two hybrid approaches, on several symbolic regression
benchmark problems show that the results are statistically better than the
obtained by the traditional LGP.Comment: Genetic and Evolutionary Computation Conference (GECCO) 2017, Berlin,
German
Evolutionary Computation in High Energy Physics
Evolutionary Computation is a branch of computer science with which,
traditionally, High Energy Physics has fewer connections. Its methods were
investigated in this field, mainly for data analysis tasks. These methods and
studies are, however, less known in the high energy physics community and this
motivated us to prepare this lecture. The lecture presents a general overview
of the main types of algorithms based on Evolutionary Computation, as well as a
review of their applications in High Energy Physics.Comment: Lecture presented at 2006 Inverted CERN School of Computing; to be
published in the school proceedings (CERN Yellow Report
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