. Genetic algorithms play a significant role, as search techniques for handling 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 natural evolution principles of populations. These algorithms process a population 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 me...

The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms.

Genetic algorithms (GAs) have been shown to be a promising approach for a wide range of search and optimization problems. One of the main obstacles in applying GAs to complex problems has often been the high computational cost due to their slow convergence rate. We encountered such a difficulty in an attempt to use the classical GA for estimating parameters of a metabolic model. Adopting a common strategy in the literature for addressing the problem -- integrating the GA with a complementary optimization technique, we developed a hybrid approach that combines a real-coded GA with a stochastic variant of simplex method in function optimization. Our empirical evaluations showed that the performance of our hybrid approach for the metabolic modeling problem improved those of a pure real-coded GA and an alternative simplex-GA hybrid developed by Renders and Bersini. We showed that the hybrid approach also improved GA's convergence rate for a function optimization problem. Based on an empiric...

This paper presents a real-coded memetic algorithm that applies a crossover hillclimbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the selfadaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

The experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimization problem remains an open question. It seems that the most promising approach at this stage of research is experimental# involving a design of a scalable test suite of constrained optimization problems# in which many features could be easily tuned. Then it would be possible to evaluate merits and drawbacks of the available methods as well as test new methods e#ciently. In this paper we propose such a test#case generator for constrained parameter optimization tech# niques. This generator is capable of creating various test problems with di#erent characteristics# like #1# problems with di#erent relative size of the feasible region in the search space# #2# problems with di#erent number and types of constraints# #3# problems with convex or non#convex objective function# possibly with multiple optima# #4# problems with highly non#...

: This report develops a Boltzmann learning refined evolution method to perform model reduction for systems and control engineering applications. The evolutionary technique offers the global search power from the "generational" Darwinism combined with "biological" Lamarckism. The evolution is further enhanced by interactive fine-learning realised by Boltzmann selection in a simulated annealing manner. This hybrid evolution program overcomes the well-known problems of chromosome stagnation and weak local exploration of a pure evolutionary algorithm. The use of one-integer-one-parameter coding scheme reduces chromosome length and improves efficiency dramatically. Enabled by a control gene as a structural switch, this indirectly guided reduction method is capable of simultaneously recommending both an optimal order number and corresponding parameters. It is uniformly applicable to both continuous and discrete time systems in both the time and the frequency domains. Three examples verify t...

This paper examines the use of a probabilistic simplex operator for asynchronous genetic search on the BOINC volunteer computing framework. This algorithm is used to optimize a computationally intensive function with a continuous parameter space: finding the optimal fit of an astronomical model of the Milky Way galaxy to observed stars. The asynchronous search using a BOINC community of over 1,000 users is shown to be comparable to a synchronous continuously updated genetic search on a 1,024 processor partition of an IBM BlueGene/L supercomputer. The probabilistic simplex operator is also shown to be highly effective and the results demonstrate that increasing the parents used to generate offspring improves the convergence rate of the search. Additionally, it is shown that there is potential for improvement by refining the range of the probabilistic operator, adding more parents, and generating offspring differently for volunteered computers based on their typical speed in reporting results. The results provide a compelling argument for the use of asynchronous genetic search and volunteer computing environments, such as BOINC, for computationally intensive optimization problems and, therefore, this work opens up interesting areas of future research into asynchronous optimization methods.

In real-coded genetic algorithms, some crossover operators do not work well on functions which have their optimum at the corner of the search space. To cope with this problem, we have proposed a boundary extension methods which allows individuals to be located within a limited space beyond the boundary of the search space. In this paper, we give an analysis of the boundary extension methods from the view point of sampling bias and perform a comparative study on the effect of applying two boundary extension methods, namely the boundary extension by mirroring BEM) and the boundary extension with extended selection (BES). We were able to confirm that to use sampling methods which have smaller sampling bias had good performance on both functions which have their optimum at or near the boundaries of the search space, and functions which have their optimum at the center of the search space. The BES/SD/A (BES by shortest distance selection with aging) had good performance on functions which have their optimum at or near the boundaries of the search space. We also confirmed that applying the BES/SD/A did not cause any performance degradation on functions which have their optimum at the center of the search space. 1.

Previous studies have proposed simplex crossover (SPX) for real-coded GAs. In this paper, we propose two types of linkage identification for simplex crossover; linkage identification with singlestage evolution (LISS) and linkage identification with multi-stage evolution (LIMS), and perform their comparative study. Results showed LIMS has more stable performance than LISS. I.

Genetic algorithms(GA) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian learning. Traditional schema theory does not support Lamarckian learning, i.e., forcing the genetic representation to match the solution found by the improvement procedure. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses improvement procedures to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper empirically examines the issues of using Lamarckian and Baldwinian learning in hybrid GAs. In the empirical investigation cond...