Catálogo de publicaciones - libros

The paper proposed a novel quantum-inspired genetic algorithm with only one chromosome, which we called Single-Chromosome Quantum Genetic algorithm (SCQGA). In SCQGA, by bringing the information representation in quantum computing into the algorithm, only one quantum chromosome (QC) is used to represent all possible states of the entire population. A novel quantum evolution method without using conventional genetic operators such as crossover operator and mutation operator is proposed, in which according to the best individuals generated by QC we adjust the quantum probability amplitude with quantum rotation gates so that the QC can produce more promising individuals with higher probability in the next generation. The paper indicated that SCQGA is a new approach belonging to estimation of distribution algorithms (EDAs). Experiments on solving a class of combinatorial optimization problems show that SCQGA performs better than conventional genetic algorithm.

In this paper, we will introduce an evolutionary algorithm for finding approximate solutions to the Weighted Minimum Hitting Set Problem. The proposed genetic algorithm, denoted by HEAT-V, makes use of a newly defined concept of virus. We will test its performance against a well known and efficient greedy algorithm, and on several families of sets.

This paper presents a genetic algorithm hybridized with a constructive procedure and reports its application on the crosstalk aware track assignment problem. In this algorithm, only dominating elements are encoded as chromosomes, on which genetic operators work to explore the solution space, while other elements are determined using constructive method. With proper dominating elements identification, the proposed approach essentially searches a much smaller space without trivial operations, efficiently generating competitive solutions with an effective constructive procedure. Experimental results on a set of industrial instances and ISPD98 benchmarks show that the proposed algorithm reduces both capacitive and inductive coupling in acceptable running time. It is probable that the proposed approach provides a practical way for the application of genetic algorithm on large scale engineering problems.

Inspired by the overlap gene expression in biological study, this paper proposes a novel evolutionary algorithm-EAOGE i.e. Evolutionary Algorithm based on Overlapped Gene Expression. Different from existing works, EAOGE suggests a new expression structure of genes with probabilities of overlapped expression for some segments. The main contributions are: (1) Proposing a novel model and an algorithm of gene expression while borrowing some ideas from artificial immunity algorithm; (2) Analyzing the expressing space and encode characteristic of the new model; (3) The extensive experiments in function finding shows that new model is 2.8~9.7 times faster than usual GEP method, and in higher-degree polynomial function finding, the success rate of EAOGE is over 10 times than usual GEP.

In this paper, a hybrid system is developed by evolving Case-Based Reasoning (CBR) with Genetic Algorithm (GA) for reverse sales forecasting of returning books. CBR systems have been successfully applied in several domains of artificial intelligence. However, in conventional CBR method each factor has the same weight which means each one has the same influence on the output data that does not reflect the practical situation. In order to enhance the efficiency and capability of forecasting in CBR systems, we applied the GAs method to adjust the weights of factors in CBR systems, GA/CBR for short. The case base of this research is acquired from a book wholesaler in Taiwan, and it is applied by GA/CBR to forecast returning books. The result of the prediction of GA/CBR was compared with other traditional methods.

A new algorithm, the immune quantum-inspired genetic algorithm (IQGA), is proposed by introducing immune concepts and methods into quantum-inspired genetic algorithm (QGA). In application to the knapsack problem, which is a well-known combinatorial optimization problem, the proposed algorithm performs better than the conventional GA (CGA), the immune GA (IGA) and QGA.

With more and more interactions between high-level and physical-level design, incremental floorplan is becoming a must to deal with such complexity. In this paper, we propose a hierarchical approach for incremental floorplan based on genetic algorithms. It combines the power of genetic optimization and partition algorithms to provide smooth controllable quality/runtime tradeoffs. Experiments show that our hierarchy approach can provide magnitudes of speedup compared to traditional flatten floorplan using genetic algorithms without much area overhead. Furthermore, incremental change is also supported in such a hierarchical floorplanner, which makes it very promising to be used in the high-level analysis and synthesis environment.

Simple genetic algorithms on populations of -binary words usually become iterative systems on 2 dimensional spaces when populations have size infinite. However, in a particular model ( model) previously introduced, it has been shown that the iterative system works in a -dimensional space.

In this paper we propose a simplification of the model and we analyze it in the case of large but finite-size populations. In particular:

Based on the concept of organization in economics, a novel genetic algorithm, organizational nonlinear genetic algorithm (ONGA), is proposed to solve global numerical optimization problems with continuous variables. In ONGA, genetic operators do not act on individuals directly, but on organizations, and four genetic operators,organization establish, organization classify, multi-parent crossover, and multi-parent mutation operators, are designed for organizations. Simulation results indicate that ONGA performs much better than the real-coded genetic algorithm both in the quality of solution and in the computational complexity.

This paper proposes an elite crossover strategy together with a dynastic change strategy for genetic algorithms. These strategies are applied to the elites, with a different crossover operation applied to the general population. This multi-crossover operation approach is different from the traditional genetic algorithms where the same crossover strategy is used on both elites and general population. The advantage of adopting a multi-crossover operation approach is faster convergence. Additionally, by adopting a dynastic change strategy in the elite crossover operation, the problem of premature convergence does not need to be actively corrected. The inspiration for the dynastic change strategy comes from ancient Chinese history where royal members of a dynasty undertake intermarriages with other royal members in order to enhance their ascendancy. The central thesis of our elite crossover strategy is that a dynasty can never be sustained forever in a society that changes continuously with its environment. A set of 8 benchmark functions is selected to investigate the effectiveness and efficiency of the proposed genetic algorithm.