Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computer-based problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and differences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research.

Genetic algorithms rely on two genetic operators - crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other.

Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of implementing search. With their commonalities come shared problems whose solutions can be investigated at a higher level and applied to both. One such problem is the manipulation of solution parameters whose values encode a desirable sub-solution. In this paper, we define a superset of evolutionary programming and genetic algorithms, called evolutionary algorithms, and demonstrate a method of automatic modularization that protects promising partial solutions and speeds acquisition time. 1. Introduction Evolutionary programming (EP) (Fogel 1992; Fogel et. al. 1966) and genetic algorithms (GAs) (Holland 1966; Goldberg 1989) have borrowed little from each other. But there are many levels at which EP and GAs are similar. For instance, both employ an analogy to natural selection over a population to search through a sp...

Genetic Programming is applied to the task of evolving general iterative sorting algorithms. A connection between size and generality was discovered. Adding inverse size to the fitness measure along with correctness not only decreases the size of the resulting evolved algorithms, but also dramatically increases their generality and thus the effectiveness of the evolution process. In addition, a variety of differing problem formulations are investigated and the relative probability of success for each is reported. An example of an evolved sort from each problem formulation is presented, and an initial attempt is made to understand the variations in difficulty resulting from these differing problem formulations. 1 Introduction In order to further the application of Genetic Programming to evolution of complex algorithms, the work reported here explores the impact of differing problem formulations and fitness measures on the likelihood of evolving a general sorting algorithm on a given G...

Introduction In this chapter we explore the lower level implementation issues surrounding what we call the Genome Interpreter. Provided is example code from 5 test programs which were used to evaluate performance. Section 13.8 summarizes the results of these tests and discusses the trade-offs involved with the various implementations. For the upcoming discussion, what we call an interpreter specifies the following lower level aspects of the design: . the raw node representation . how a tree of nodes is represented . the method for evaluating an individual node . the method for evaluating the tree as a whole . the methods for (or methods to assist) those genetic operators which are dependent on the node or tree representation. A key point is that the interpreter specifies the node implementation which is the particular part of the platform-coding in which the overhead will be magnified. Therefore, the interpreter is the most crucial component in the overall design with respect t

Abstract—Abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the most probable configurations given observed evidence. When we are interested only in a subset of the network’s variables, this problem is called partial abductive inference. Both problems are NP-hard and so exact computation is not always possible. In this paper, a genetic algorithm is used to perform partial abductive inference in BBNs. The main contribution is the introduction of new genetic operators designed specifically for this problem. By using these genetic operators, we try to take advantage of the calculations previously carried out, when a new individual is evaluated. The algorithm is tested using a widely used Bayesian network and a randomly generated one and then compared with a previous genetic algorithm based on classical genetic operators. From the experimental results, we conclude that the new genetic operators preserve the accuracy of the previous algorithm, and also reduce the number of operations performed during the evaluation of individuals. The performance of the genetic algorithm is, thus, improved. Index Terms—Abductive inference, bayesian belief networks, evolutionary computation, genetic operators, most probable explanation, probabilistic reasoning. I.

Simulated Annealing (SA) is a general stochastic search algorithm. It is usually employed as an optimization method to find a near optimal solution for hard combinatorial optimization problems, but it is very difficult to give the accuracy of the solution found. In order to find a better solution, an often used strategy is to run the algorithm many times and select the best solution as the final one. This paper gives an algorithm called Genetic Annealing (GA), which connects each run of SA and gradually improve the solution. It introduces the concept of evolution into the annealing process. The basic idea is to use genetic operations adopted in genetic algorithms to inherit the possible benefits of the solutions found in former runs. Experiments have shown that GA is better than classical SA. The parallelization of GA is also discussed in the paper. keywords --- Simulated Annealing, Genetic Algorithms, Combinatorial Optimization. 1 Introduction SA is a general stochastic search metho...

Abstract. This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.

The determination of the sequence of all nucleotide base-pairs in a DNA molecule, from restriction-fragment data, is a complex task and can be posed as the problem of finding the optima of a multi-modal function. A genetic algorithm that uses multi-niche crowding permits us to do this. Performance of this algorithm is first tested using a standard suite of test functions. The algorithm is next tested using two data sets obtained from the Human Genome Project at the Lawrence Livermore National Laboratory. The new method holds promise in automating the sequencing computations.

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