Rudrapatna S. Ramakrishna

A genetic algorithm for shortest path routing problem and the sizing of populations

This paper presents a genetic algorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. This paper also develops a population-sizing equation that facilitates a solution with desired quality. It is based on the gambler ruin model; the equation has been further enhanced and generalized. The equation relates the size of the population, quality of solution, cardinality of the alphabet, and other parameters of the proposed algorithm. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types for almost all source-destination pairs. Furthermore, simulation studies emphasize the usefulness of the population-sizing equation. The equation scales to larger networks. It is felt that it can be used for determining an adequate population size in the SP routing problem.

Elitism-based compact genetic algorithms

This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient cGAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is to deal with issues connected with lack of memory by allowing a selection pressure that is high enough to offset the disruptive effect of uniform crossover. The pe-cGA finds a near optimal solution (i.e., a winner) that is maintained as long as other solutions generated from probability vectors are no better. The ne-cGA further improves the performance of the pe-cGA by avoiding strong elitism that may lead to premature convergence. It also maintains genetic diversity. This paper also proposes an analytic model for investigating convergence enhancement.

Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World

This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, real-valued optimization problems quickly, accurately, and reliably. This is the real-coded Bayesian optimization algorithm (rBOA). The objective is to bring the strength of (discrete) BOA to bear upon the area of real-valued optimization. That is, the rBOA must properly decompose a problem, efficiently fit each subproblem, and effectively exploit the results so that correct linkage learning even on nonlinearity and probabilistic building-block crossover (PBBC) are performed for real-valued multivariate variables. The idea is to perform a Bayesian factorization of a mixture of probability distributions, find maximal connected subgraphs (i.e. substructures) of the Bayesian factorization graph (i.e., the structure of a probabilistic model), independently fit each substructure by a mixture distribution estimated from clustering results in the corresponding partial-string space (i.e., subspace, subproblem), and draw the offspring by an independent subspace-based sampling. Experimental results show that the rBOA finds, with a sublinear scale-up behavior for decomposable problems, a solution that is superior in quality to that found by a mixed iterative density-estimation evolutionary algorithm (mIDEA) as the problem size grows. Moreover, the rBOA generally outperforms the mIDEA on well-known benchmarks for real-valued optimization.

On the Scalability of Real-Coded Bayesian Optimization Algorithm

Estimation of distribution algorithms (EDAs) are major tools in evolutionary optimization. They have the ability to uncover the hidden regularities of problems and then exploit them for effective search. Real-coded Bayesian optimization algorithm (rBOA) which brings the power of discrete BOA to bear upon the continuous domain has been regarded as a milestone in the field of numerical optimization. It has been empirically observed that the rBOA solves, with subquadratic scaleup behavior, numerical optimization problems of bounded difficulty. This underlines the scalability of rBOA (at least) in practice. However, there is no firm theoretical basis for this scalability. The aim of this paper is to carry out a theoretical analysis of the scalability of rBOA in the context of additively decomposable problems with real-valued variables. The scalability is measured by the growth of the number of fitness function evaluations (in order to reach the optimum) with the size of the problem. The total number of evaluations is computed by multiplying the population size for learning a correct probabilistic model (i.e., population complexity) and the number of generations before convergence, (i.e., convergence time complexity). Experimental results support the scalability model of rBOA. The rBOA shows a subquadratic (in problem size) scalability for uniformly scaled decomposable problems.

visPerf: monitoring tool for grid computing

It is difficult to see the status of a working production grid system without a customized monitoring system. Most grid middleware provide simple system monitoring tools, or simple tools for checking system status. visPerf is a general purpose grid monitoring tool for visualizing, investigating, and controlling the system in a distributed manner. visPerf is a system based on a distributed monitoring sensor, visSensor, in which the sensor uses methods to monitor the status of grid middleware with little or no modifications to the underlying system.

Multiple-deme parallel estimation of distribution algorithms: Basic framework and application

This paper presents a basic framework that facilitates the development of new multiple-deme parallel estimation of distribution algorithms (PEDAs). The aim is to carry over the migration effect that arises in multiple-deme parallel genetic algorithms (PGAs) into probability distribution of EDAs. The idea is to employ two kinds of probability vector (PV): one each for resident and immigrant candidates. The distribution of crossbred individuals (that virtually exist on both kinds of PV) is then utilized by a new type of crossover, the PV-wise crossover. A multiple-deme parallel population-based incremental learning (P2BIL) scheme is proposed as an application. The P2BIL scheme closely follows the proposed framework that includes a new learning strategy (i.e., PV update rule). Experimental results show that P2BIL generally exhibits solutions that compare favourably with those computed by an existing PGA with multiple demes, thereby supporting the validity of the proposed framework for designing multiple-deme PEDAs.

Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservation

This paper provides empirical studies on MrBOA, which have been designed for strengthening diversity of nondominated solutions. The studies lead to modified sharing. A new selection scheme has been suggested for improving diversity performance. Empirical tests validate their effectiveness on uniformity and front-spread (i.e., diversity) of nondominated set. A diversity-preserving MrBOA (dp-MrBOA) has been designed by carefully combining all the promising components; i.e., modified sharing, dynamic crowding, and diversity-preserving selection. Experiments demonstrate that the dp-MrBOA is able to significantly improve diversity performance (for the scaling problems), without weakening proximity of nondominated set.

A genetic-inspired multicast routing optimization algorithm with bandwidth and end-to-end delay constraints

This paper presents a genetic-inspired multicast routing algorithm with Quality of Service (i.e., bandwidth and end-to-end delay) constraints. The aim is to efficiently discover a minimum-cost multicast tree (a set of paths) that satisfactorily helps various services from a designated source to multiple destinations. To achieve this goal, state of the art genetic-based optimization techniques are employed. Each chromosome is represented as a tree structure of Genetic Programming. A fitness function that returns a tree cost has been suggested. New variation operators (i.e., crossover and mutation) are designed in this regard. Crossover exchanges partial chromosomes (i.e., sub-trees) in a positionally independent manner. Mutation introduces (in part) a new sub-tree with low probability. Moreover, all the infeasible chromosomes are treated with a simple repair function. The synergy achieved by combing new ingredients (i.e., representation, crossover, and mutation) offers an effective search capability that results in improved quality of solution and enhanced rate of convergence. Experimental results show that the proposed GA achieves minimal spanning tree, fast convergence speed, and high reliability. Further, its performance is better than that of a comparative reference.

A Memory-Efficient Elitist Genetic Algorithm

This paper proposes a memory-efficient elitist genetic algorithm (me2GA) for solving hard optimization problems quickly and effectively. The idea is to properly reconcile multiple probability (distribution) vectors (PVs) with elitism. Multiple PVs (rather than a single PV as in compact GA (cGA)) provide an effective framework for representing the population as a probability distribution over the set of solutions. A coordinated interplay amongst multiple PVs maintains genetic diversity, thereby recovery from decision errors is possible. On the other hand, reconciling with elitism allows a potentially optimal (elitist) solution to be kept current as long as other (competing) solutions generated from PVs are no better. This is because it exerts a selection pressure that is high enough to offset the disruptive effects of uniform crossover. It also attempts to adaptively alter the selection pressure in accordance with the degree of problem difficulty through pair-wise tournament selection strategy. Experimental results show that the proposed algorithm generally exhibits a superior quality of solution. Moreover, the proposed algorithm deploys memory more efficiently than extant sGA and cGA, especially when the problem is difficult.

Adaptive Clustering Technique Using Genetic Algorithms

This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.