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Genetic Evolution of Control Systems
Abstract
In this paper, we present to utilize Genetic Algorithms (GAs) as tools to model control processes. Two different crossover operators are combined during evolution to maintain population diversity and to sustain local improvement in the search space. In this manner, a balance between global exploration and local exploitation is reserved during genetic search. To verify the efficiency of the proposed method, the desired control sequences of a given system are solved by the optimal control theory as well as GA with hybrid crossovers to compare their performances. The experimental results showed that the control sequences obtained from the proposed GA with hybrid crossovers are quite consistent with the results of the optimal control.
... MIPP also can be solved by convergent outer approximation method by reducing it to a series of sub-problems [3,4]. In this case, the general procedures and the linearization or nor-linearization of some of MIPP's sub-problems need some special characteristic, such as convexity and seperatability [5]. As many realistic problems are non-smooth and even discontinuous at some points in their fields, applications of these methods are limited. ...
This paper proposed a chaotic genetic algorithm (CGA) to solve the mixed integer programming problem (MIPP). The basic idea of this algorithm is to overcome the deficiency of genetic algorithm (GA) by introducing chaotic disturbances into the genetic search process. Two typical MIPP problems are used to evaluate the performances of the proposed CGA. Experimental results show that performances of the algorithm have been improved by the chaotic disturbances, such as, search ability, precision, stability and convergence speed or calculation efficiency. The proposed CGA algorithm is suitable for solving complicated practical MIPP problem.
Genetic algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the naturalevolution principles of populations. These algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.
Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.
Genetic algorithms (GAs) are efficient and robust search methods
that are being employed in a plethora of applications with extremely
large search spaces. The directed search mechanism employed in GAs
performs a simultaneous and balanced exploration of new regions in the
search space and exploitation of already-discovered regions. This paper
introduces the notion of fitness moments for analyzing the working of
GAs. We show that the fitness moments in any generation may be predicted
from those of the initial population. Since a knowledge of the fitness
moments allows us to estimate the fitness distribution of strings, this
approach provides for a method of characterizing the dynamics of GAs. In
particular, the average fitness and fitness variance of the population
in any generation may be predicted. We introduce the technique of
fitness-based disruption of solutions for improving the performance of
GAs. Using fitness moments, we demonstrate the advantages of using
fitness-based disruption. We also present experimental results comparing
the performance of a standard GA and two other GAs (the controlled
disruption GA and the adaptive GA) that incorporate the principle of
fitness-based disruption. The experimental evidence clearly demonstrates
the power of fitness-based disruption
In this paper we describe an efficient approach for multimodal
function optimization using genetic algorithms (GAs). We recommend the
use of adaptive probabilities of crossover and mutation to realize the
twin goals of maintaining diversity in the population and sustaining
the, convergence capacity of the GA. In the adaptive genetic algorithm
(AGA), the probabilities of crossover and mutation, pc and p
m, are varied depending on the fitness values of the
solutions. High-fitness solutions are `protected', while solutions with
subaverage fitnesses are totally disrupted. By using adaptively varying
pc and p, we also provide a solution to the
problem of deciding the optimal values of pc and
pm, i.e., pc and pm need not be
specified at all. The AGA is compared with previous approaches for
adapting operator probabilities in genetic algorithms. The Schema
theorem is derived for the AGA, and the working of the AGA is analyzed.
We compare the performance of the AGA with that of the standard GA (SGA)
in optimizing several nontrivial multimodal functions with varying
degrees of complexity
Recent research on the use of genetic algorithms for training neural networks has led to controversy as to whether or not this approach is more efficient than the more traditional backpropagation algorithm. Each of these approaches has developed from different backgrounds and each has its own set of advantages and disadvantages. In this paper, we propose three genetic algorithms, developed specifically for the task of training layered feedforward neural networks, that use an adaptive technique which takes advantage of the network architecture. We also describe several simulation experiments which were conducted to assess the genetic algorithms and to compare them with backpropagation. The experimental results show that our algorithms are more efficient in terms of speed of convergence. However, we recognize that a hybrid approach which incorporates the advantages of both the backpropagation and genetic algorithm techniques might be a more practical approach.
Introduction Cleveland and Smith's Approach Gupta, Gupta, and Kumar's Approach Lee and Kim's Approach Cheng and Gen's Approach
In this paper we introduce interval-schemata as a tool for analyzing real-coded genetic algorithms (GAs). We show how interval-schemata are analogous to Holland's symbol-schemata and provide a key to understanding the implicit parallelism of real-valued GAs. We also show how they support the intuition that real-coded GAs should have an advantage over binary coded GAs in exploiting local continuities in function optimization. On the basis of our analysis we predict some failure modes for real-coded GAs using several different crossover operators and present some experimental results that support these predictions. We also introduce a crossover operator for real-coded GAs that is able to avoid some of these failure modes.
This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. The remainder of the book is turned over to a series of short review articles by a collection of authors, each explaining how genetic algorithms have been applied to problems in their own specific area of interest. The first part of the book introduces the fundamental genetic algorithm (GA), explains how it has traditionally been designed and implemented and shows how the basic technique may be applied to a very simple numerical optimisation problem. The basic technique is then altered and refined in a number of ways, with the effects of each change being measured by comparison against the performance of the original. In this way, the reader is provided with an uncluttered introduction to the technique and learns to appreciate why certain variants of GA have become more popular than others in the scientific community. Davis stresses that the choice of a suitable representation for the problem in hand is a key step in applying the GA, as is the selection of suitable techniques for generating new solutions from old. He is refreshingly open in admitting that much of the business of adapting the GA to specific problems owes more to art than to science. It is nice to see the terminology associated with this subject explained, with the author stressing that much of the field is still an active area of research. Few assumptions are made about the reader's mathematical background. The second part of the book contains thirteen cameo descriptions of how genetic algorithmic techniques have been, or are being, applied to a diverse range of problems. Thus, one group of authors explains how the technique has been used for modelling arms races between neighbouring countries (a non- linear, dynamical system), while another group describes its use in deciding design trade-offs for military aircraft. My own favourite is a rather charming account of how the GA was applied to a series of scheduling problems. Having attempted something of this sort with Simulated Annealing, I found it refreshing to see the authors highlighting some of the problems that they had encountered, rather than sweeping them under the carpet as is so often done in the scientific literature. The editor points out that there are standard GA tools available for either play or serious development work. Two of these (GENESIS and OOGA) are described in a short, third part of the book. As is so often the case nowadays, it is possible to obtain a diskette containing both systems by sending your Visa card details (or $60) to an address in the USA.
As control system tasks become more demanding, more robust
controller design methodologies are needed. A genetic algorithm (GA)
optimizer, which utilizes natural evolution strategies, offers a
promising technology that supports optimization of the parameters of
fuzzy logic and other parameterized nonlinear controllers. This article
shows how GAs can effectively and efficiently optimize the performance
of fuzzy net controllers employing high performance simulation to reduce
the design cycle time from hours to minutes. Our results demonstrate the
robustness of a GA-based computer-aided system design methodology for
rapid prototyping of control systems
Fuzzy modeling using fuzzy neural networks with genetic algorithms
- C Zhou
- J Ye
- S Zhu