Daryl Essam

Multi-operator based evolutionary algorithms for solving constrained optimization problems

Over the last two decades, many sophisticated evolutionary algorithms have been introduced for solving constrained optimization problems. Due to the variability of characteristics in different COPs, no single algorithm performs consistently over a range of problems. In this paper, for a better coverage of the problem characteristics, we introduce an algorithm framework that uses multiple search operators in each generation. The appropriate mix of the search operators, for any given problem, is determined adaptively. The framework is tested by implementing two different algorithms. The performance of the algorithms is judged by solving 60 test instances taken from two constrained optimization benchmark sets from specialized literature. The first algorithm, which is a multi-operator based genetic algorithm (GA), shows a significant improvement over different versions of GA (each with a single one of these operators). The second algorithm, using differential evolution (DE), also confirms the benefit of the multi-operator algorithm by providing better and consistent solutions. The overall results demonstrated that both GA and DE based algorithms show competitive, if not better, performance as compared to the state of the art algorithms.

Memetic algorithms for solving job-shop scheduling problems

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. In this paper, we developed a memetic algorithm (MA) for solving JSSPs. Three priority rules were designed, namely partial re-ordering, gap reduction and restricted swapping, and used as local search techniques in our MA. We have solved 40 benchmark problems and compared the results obtained with a number of established algorithms in the literature. The experimental results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time.

An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems

Many real-world optimization problems are difficult to solve as they do not possess the nice mathematical properties required by the exact algorithms. Evolutionary algorithms are proven to be appropriate for such problems. In this paper, we propose an improved differential evolution algorithm that uses a mix of different mutation operators. In addition, the algorithm is empowered by a covariance adaptation matrix evolution strategy algorithm as a local search. To judge the performance of the algorithm, we have solved well-known benchmark as well as a variety of real-world optimization problems. The real-life problems were taken from different sources and disciplines. According to the results obtained, the algorithm shows a superior performance in comparison with other algorithms that also solved these problems.

Grammar model-based program evolution

In evolutionary computation, genetic operators, such as mutation and crossover, are employed to perturb individuals to generate the next population. However these fixed, problem independent genetic operators may destroy the sub-solution, usually called building blocks, instead of discovering and preserving them. One way to overcome this problem is to build a model based on the good individuals, and sample this model to obtain the next population. There is a wide range of such work in genetic algorithms; but because of the complexity of the genetic programming (GP) tree representation, little work of this kind has been done in GP. In this paper, we propose a new method, grammar model-based program evolution (GMPE) to evolved GP program. We replace common GP genetic operators with a probabilistic context-free grammar (SCFG). In each generation, an SCFG is learnt, and a new population is generated by sampling this SCFG model. On two benchmark problems we have studied, GMPE significantly outperforms conventional GP, learning faster and more reliably.

A new genetic algorithm for solving optimization problems

Over the last two decades, many different genetic algorithms (GAs) have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms has shown consistent performance over a range of real world problems. The success of any GA depends on the design of its search operators, as well as their appropriate integration. In this paper, we propose a GA with a new multi-parent crossover. In addition, we propose a diversity operator to be used instead of mutation and also maintain an archive of good solutions. Although the purpose of the proposed algorithm is to cover a wider range of problems, it may not be the best algorithm for all types of problems. To judge the performance of the algorithm, we have solved aset of constrained optimization benchmark problems, as well as 14 well-known engineering optimization problems. The experimental analysis showed that the algorithm converges quickly to the optimal solution and thus exhibits a superior performance in comparison to other algorithms that also solved those problems.

GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems

Over the last two decades, many Genetic Algorithms have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms performs consistently over a range of problems. In this paper, we introduce a GA with a new multi-parent crossover for solving a variety of optimization problems. The proposed algorithm also uses both a randomized operator as mutation and maintains an archive of good solutions. The algorithm has been applied to solve the set of real world problems proposed for the IEEE-CEC2011 evolutionary algorithm competition.

Fitness inheritance for noisy evolutionary multi-objective optimization

This paper compares the performance of anti-noise methods, particularly probabilistic and re-sampling methods, using NSGA2. It then proposes a computationally less expensive approach to counteracting noise using re-sampling and fitness inheritance. Six problems with different difficulties are used to test the methods. The results indicate that the probabilistic approach has better convergence to the Pareto optimal front, but it looses diversity quickly. However, methods based on re-sampling are more robust against noise but they are computationally very expensive to use. The proposed fitness inheritance approach is very competitive to re-sampling methods with much lower computational cost.

Representation and structural difficulty in genetic programming

Standard tree-based genetic programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard genetic programming, which is itself a consequence of the fixed-arity property embodied in its representation.

Software project effort estimation using genetic programming

Knowing the estimated cost of a software project early in the development cycle is a valuable asset for management. In this paper, an evolutionary computation method, grammar guided genetic programming (GGGP), is used to fit models, with the aim of improving the prediction of software development costs. Valuable results are obtained, significantly better than those obtained by simple linear regression. In this research, GGGP, because of its flexibility and the ability of incorporating background knowledge, also shows great potential in being applied in other software engineering modeling problems.

Localized genetic algorithm for vehicle routing problem with time windows

This paper introduces the Localized Optimization Framework (LOF). This framework is an iterative procedure between two phases, Optimization and De-optimization. Optimization is done on the problem parts rather than the problem as a whole, while de-optimization is done on the whole problem. To test our hypothesis, we have chosen a genetic algorithm as an optimization methodology and Vehicle Routing Problem with Time Windows (VRPTW) as a domain space. We call this new scheme the Localized Genetic Algorithm (LGA). We demonstrate that the LGA is, on average, able to produce better solutions than most of the other heuristics on small scale problems of VRPTW. Furthermore the LGA has attained several new best solutions on popular datasets.