Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve amongst others regression, classification, and time-series forecasting problems. A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still...

Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA d...

Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA d...

Genetic algorithms (GAs) are multi-dimensional, blind heuristic search methods that involve complex interactions among parameters (such as population size, number of generations, GA operators and operator probabilities). The question whether the quality of results obtained by GAs depend upon the values given to these parameters, is a matter of research interest. This work studies the problem of...

It has been proved that the hardware/software partitioning problem is NP-hard. Currently we have tried a variety of computerized algorithms to resolve it, which can be divided into two major categories: accurate algorithms and heuristic algorithms. This paper will discuss accurate algorithms and heuristic algorithms respectively. Accurate algorithms take the example of a greedy algorithm. It ab...

This paper deals with some new operators of genetic algorithms for solving the traveling salesman problem (TSP). These include a new operator called, ”nearest fragment operator” based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. Superiority of these operators has been established on different benchmark data sets for symmetric TSP. Finally, th...

This paper presents a new Memetic Algorithm designed to compute near optimal solutions for the MinLA problem. It incorporates a highly specialized crossover operator, a fast MinLA heuristic used to create the initial population and a local search operator based on a fine tuned Simulated Annealing algorithm. Its performance is investigated through extensive experimentation over well known benchm...

This paper presents a new heuristic algorithm for the graph coloring problem based on a combination of genetic algorithms and simulated annealing. Our algorithm exploits a novel crossover operator for graph coloring. Moreover, we investigate various ways in which simulated annealing can be used to enhance the performance of an evolutionary algorithm. Experiments performed on various collections...

The Linear Ordering Problem(LOP), which is a well-known NP-hard problem, has numerous applications in various fields. Using this problem as an example, we illustrate a general procedure of designing a hybrid genetic algorithm, which includes the selection of crossover/mutation operators, accelerating the local search module and tuning the parameters. Experimental results show that our hybrid ge...

background  & aim: case-crossover  studies are the case-control  version of crossover studies. in these studies, cases and controls are the same subjects. the term crossover is applied for designs that all subjects pass through both treatment (exposure) and placebo (non-exposure) phases. in fact they are  crossover  of  subjects  between  periods  of  exposure  and  non-exposure.  this  design ...