Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness these EAs comes from components and parameters. These solution representation, selection, crossover, mutation. Unfortunately, almost all EA based complex de...

Local optima networks are a compact representation of fitness landscapes that can be used for analysis and visualisation. This paper provides the first analysis of the Asymmetric Travelling Salesman Problem using local optima networks. These are generated by sampling the search space by recording the progress of an existing evolutionary algorithm based on the Generalised Asymmetric Partition Cr...

Facility Layout Problem (FLP) is logic based combinatorial optimization problem. It is a meta-heuristic solution approach that gained significant attention to obtained optimal facility layout. This paper examines the convergence analysis by changing the crossover and mutation probability in an optimal facility layout. This algorithm is based on appropriate techniques that include multipoint swa...

Minimum vertex cover problem (MVCP) is an NP-hard problem and it has numerous real life applications. This paper presents hybrid genetic algorithm (HGA) to solve MVCP efficiently. In this paper we have demonstrated that when local optimization technique is added to genetic algorithm to form HGA, it gives near to optimal solution speedy. We have developed new heuristic vertex crossover operator ...

The goal of np-hard Combinatorial Optimization is finding the best possible solution from the set of feasible solutions. In this paper, we establish an approach using genetic algorithm with various selection and crossover operators with repair function for an institute course timetabling problem. It employs a constructive heuristic approach to find the feasible timetable, fitness value

Purpose: In this paper, we consider the maximum traveling salesman problem, a variation of the usual traveling salesman problem, in which the objective is to maximize the cost of a tour of the salesman. The main purpose of this paper is to develop a hybrid genetic algorithm (GA) for obtaining a heuristically optimal solution to the problem. Methods: First, a simple GA and then a hybrid GA have ...

A rtificial bee Colony algorithm (ABC) is a population based heuristic search technique used for optimization problems. ABC is a very effective optimization technique for continuous optimization problem. Crossover operators have a better exploration property so crossover operators are added to the ABC. This paper presents ABC with different types of real coded crossover operator and its applica...

We introduce a new genetic algorithm (GA) approach for the integrated inventory distribution problem (IIDP). We present the developed genetic representation and use a randomized version of a previously developed construction heuristic to generate the initial random population. We design suitable crossover and mutation operators for the GA improvement phase. The comparison of results shows the s...

Path cost optimization is essential for maneuvering vehicles in a cost effective way. The term cost can be interpreted as fuel consumption, path visibility, probability of being detected, probability of being attacked or a combination of the above. Exact algorithms such as linear programming and dynamic programming can always provide globally optimum solution to such a problem. However, as the ...