Catálogo de publicaciones - libros

Two binary-encoded models describing some aspects of the coevolution between an artificial immune system and a set of antigens have been proposed and analyzed. The first model has focused on the coevolution between antibodies generating gene libraries and antigens. In the second model, the coevolution involves a new population of self molecules whose function was to establish restrictions in the evolution of libraries’ population. A coevolutionary genetic algorithm (CGA) was used to form adaptive niching inspired in the Coevolutionary Shared Niching strategy. Numerical experiments and conclusions are presented.

A new algorithm of evolutionary computing, which combines clone selective algorithm involved in artificial immunity system theory and mind evolution algorithm (MEA) proposed in reference [4], is presented in this paper. Based on similartaxis which is the one of MEA operators, some operators borne by the new algorithm including such as clone mutation, clone crossover, clone selection are also introduced. Then the clone mind evolution algorithm (CMEA) is developed by using the diversity principle of antigen-antibody. Not only can CMEA converge to globally optimal solution, but also it solve premature convergence problem efficiently. The simulating results of the representative evaluation function show that the problem of degeneration phenomenon existing in GA and MEA can be perfectly solved, and the rapidity of convergence is evidently improved by CMEA studied in the paper. In the example of the solution to the numerical problem, the search range of solution is expanded and the possibility of finding the optimal solution is increased.

In this paper, Immune Mind Evolutionary Algorithm (IMEA) is introduced to correct the nonlinearity of frequency modulation (FM) of voltage controlled oscillator (VCO) in linear frequency modulation continuous wave (LFMCW) radar level gauge. Firstly, the FM voltage is divided into several subsections, then by using fast Fourier transform (FFT) for the beat frequency signals, the characteristic of the spectrum is distilled, and furthermore an evaluation function is constructed. IMEA is applied to optimize the endpoint coordinates of the subsections to get nonlinear curve of FM voltage so as to compensate for the nonlinearity of VCO. Experiments show that the proposed method has good correction performance with no requirement for additional hardware, and it can complete correction in a relative short time.

Choice of ADPCM’s step-size updating factors M has a sea capacity of computing that optimizes multi-variables with complex target function. There is the effective scheme such as GA or MEA but its convergence rate becomes too slowly in the neighborhood of peak value of multi-peak function to come away from local optimization. The Clone Mind Evolution Algorithm (CMEA) that introduces the clone operator to reserve the strong component of the weak individual to next iterativeness, which effect is very obvious with testing the typical function, comes into the MEA’s similartaxis operator and is used to optimize ADPCM’s 8 step-size updating factors. The experiment result shows that the CMEA’s SNR has been reformed average 1.03dB every generation, which is exceeding MEA’s by 0.4dB, in beginning five of iterativeness and overrun the MEA’s from generation 5. Furthermore, the MEA’s quantity of computing is equal to CMEA’s by 1.67 times and the latter is of anti-prematurely.

In this paper we propose one way of mathematical formulation of structure of molecular activity from operator algebraic view points.

A new Immune Memory Genetic Algorithm (IMGA) based on the mechanism of immune memory and immune network is proposed in this article . Using Markov chains theory, we proven that NGA(Niche Genetic Algorithms) can’t not be complete convergence but IMGA can. The contrast simulation experiments between NGA and IMGA are performed. The experiments results validate the theoretical analysis and testify that IMGA has availability on solving multi-modal optimization problems, with quickly convergence ability and wonderful stability.

The aim of this paper is to study two new forms of genetic operators: duplication and fabrication. Duplication is a reproduce procedure that will reproduce the best fit chromosome from the elite base. The introduction of duplication operator into the modified GA will speed up the convergence rate of the algorithm however the trap into local optimality can be avoided. Fabrication is an artificial procedure used to produce one or several chromosomes by mining gene structures from the elite chromosome base. Statistical inference by job assignment procedure will be applied to produce artificial chromosomes and these artificial chromosomes provides new search directions and new solution spaces for the modified GA to explore. As a result, better solution quality can be achieved when applying this modified GA. Different set of problems will be tested using modified GA by including these two new operators in the procedure. Experimental results show that the new operators are very informative in searching the state space for higher quality of solutions.

The optimization of Job Shop scheduling is very important because of its theoretical and practical significance. Much research about it has been reported in recent years. But most of them were about classical Job Shop scheduling. The existence of a gap between scheduling theory and practice has been reported in literature. This work presents a robust procedure to solve multiobjective fuzzy Job Shop scheduling problems with some more realistic constraints such as fuzzy processing time, fuzzy duedate and alternative machine constraints for jobs. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, multiobjective fuzzy Job Shop scheduling problems have been formulated as three-objective ones which not only maximize the minimum agreement index but also maximize the average agreement index and minimize the maximum fuzzy completion time. By adopting two-chromosome representation, an extended G&T algorithm which is suitable for solving the fuzzy Job Shop scheduling with alternative machines has been proposed. Finally, numerical examples are given to illustrate the effectiveness of our proposed method that provides a new way to study planning and scheduling problems in fuzzy circumstances.

In this paper, the encoding techniques of Genetic Algorithms are studied and a sufficient convergence condition on genetic encoding is presented. Some new categories of codes are defined, such as Uniform code, Bias code, Tri-sector code and Symmetric codes etc. Meanwhile, some new definitions on genetic encoding as well as some operations are presented, so that a sufficient convergence condition of GAs is inducted. Based on this study, a new genetic strategy, GASC(Genetic Algorithm with Symmetric Codes), is developed and applied in robot dynamic control and path planning. The experimental results show that the special genetic encoding techniques enhance the performance of Genetic Algorithms. The convergence speed of GASC is much faster than that of some traditional genetic algorithms. That is very significant for finding more application of GAs, as, in many cases, Genetic Algorithms’ applications are limited by their convergence speed.

Evolutionary algorithms are especially suited for multi-objective optimization problems. Many evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms are relatively scarce. This paper analyzes the convergence properties of a simple pragmatic ( μ +1)-MOEA. The convergence of MOEAs is defined and the general convergence conditions are studied. Under these conditions, it is proven that the proposed ( μ +1)-MOEA converges almost surely to the Pareto-optimal front.