While searching for suboptimal solutions for large-scale problems, it is critical to force search algorithms on promising regions. This paper presents genetic algorithms with search space reductions (RGAs) and their application to solving large-scale permutation flowshop problems. The reduced search spaces are defined by adding precedence constraints generated by heuristic rules. To balance bet...

Crossover operators that preserve common components can also preserve representation level constraints. Consequently, these constraints can be used to beneficially reduce the search space. For example, in flow shop scheduling problems with order-based objectives (e.g. tardiness costs and earliness costs), search space reductions have been implemented with precedence constraints. Experiments sho...

Expected inventory order crossovers occur if at the moment of ordering it is expected that orders will not arrive in the sequence they are ordered. Recent research has shown that (a) expected inventory order crossovers will be encountered more frequently in future, and that (b) use of a myopic order-up-to policy based on a stochastic dynamic programming approach leads to improved performance co...

Computing the bandwidth-delay-constrained least-cost multicast routing tree is an NP-complete problem. In this paper, we propose a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA). In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random...

Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Five representations are investigated for the Multidimensional Knapsack problem. Common mutation operators (like bit-flip mutation) and classic 1-point and uniform crossover are employed to generate fit...

The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature, in order to solve particular computational problems. These natural principles are: inheritance, crossover, mutation, survival of the fittest, migrations and so on. The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classe...

In this paper, we propose some genetic algorithms with adaptive abilities and compare with them. Crossover and mutation operators of genetic algorithms are used for constructing the adaptive abilities. All together four adaptive genetic algorithms are suggested: one uses a fuzzy logic controller improved in this paper and others employ several heuristics used in conventional studies. These algo...

A series of simple biases to the selection of crossover points in treestructured genetic programming are investigated with respect to the provision of parsimonious solutions. Such a set of biases has a minimal computational overhead as they are based on information already used to estimate the fitness of individuals. Reductions to code bloat are demonstrated for the real world classification pr...

Hyperclimbing is an intuitive, general-purpose, global optimization heuristic applicable to product spaces with rugged or stochastic cost functions. The strength of this heuristic lies in its insusceptibility to local optima when the cost function is deterministic, and its tolerance for noise when the cost function is stochastic. Hyperclimbing works by decimating a search space, i.e., by iterat...