Genetic Algorithms are biologically inspired optimization algorithms. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Crossover operators are used to bring diversity in the population. This paper studies different crossover operators and then proposes a hybrid crossover operator that incorporates knowledg...

These last years two global optimizations methods hybridizing Evolutionary Algorithms (EA, but mainly GA) with hill-climbing methods have been investigated. The first one involves two interwoven levels of optimization: Evolution (EA) and Individual Learning (hill-climbing), which cooperate in the global optimization process. The second one consists of modifying EA by the introduction of new gen...

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This chapter investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework o...

Binary descriptors of image patches provide processing speed advantages and require less storage than methods that encode the patch appearance with a vector of real numbers. We provide evidence that, despite its simplicity, a stochastic hill climbing descriptor construction process defeats recently proposed alternatives on a standard discriminative power benchmark. The method is easy to impleme...

This paper introduces a hybrid evolutionary hillclimbing algorithm that quickly solves (!onstraint, Satisfaction Problems (CSPs). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent, constraint network. This hybrid out,performs a well known hill-climbing algorithm, ...

In my youth I used to wander in the mountains. I would gain a "feel" of the terrain and gradually build up a reliable intuition of how to get from here to there and back again. Always, on these expeditions, I would discover special places a tiny area, the only one, where fairy slippers grew; a pool in a rushing stream that was deep enough to swim in. Invariably I would find myself excitedly cli...

In this paper, we generalize the models used by MacKay [1] in his analysis of evolutionary strategies that are based on sexual, rather than asexual, reproduction methods. This analysis can contribute to the understanding of the relative power of genetic algorithms over search methods based upon stochastic hill-climbing, e.g. [2], [3].

This paper describes the meta-level control system of a program (Dominic) for parametric design of mechanical components by iterative redesign. We view parametric design as search, and thus Dominic is a hill climbing algorithm. However, from experience with Dominic we concluded that modeling engineering design as hill climbing has several limitations. Therefore, a need for meta-level control kn...