The issue of setting the values of various parameters of an evolutionary algorithm (EA) is crucial for good performance. One way to do it is controlling EA parameters on-the-fly, which can be done in various ways and for various parameters. We briefly review these options in general and present the findings of a literature search and some statistics about the most popular options. Thereafter, w...

Hyper-heuristics are a class of high-level search methodologies which operate over a search space of heuristics rather than a search space of solutions. Hyper-heuristic research has set out to develop methods which are more general than traditional search and optimisation techniques. In recent years, focus has shifted considerably towards cross-domain heuristic search. The intention is to devel...

Genetic Programming uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees. The sufficiency principle requires that the set be sufficient to label the desired solution trees. To satisfy this principle, the user is often forced to provide a large set, which unfortunately also enlarges the repre...

The paper presents a population-based heuristic for computing approximations of the efficient solution set for the assignment problem with two objectives. The heuristic is based on the intensive use of three operators-a local search, a crossover and a path-relinking-performed on a population composed only of elite solutions. The initial population is a subset of supported solutions, where each ...

Genetic algorithms (GA) are instances of random heuristic search (RHS) which mimic biological evolution and molecular genetics in simplified form. These random heuristic search algorithms can be theoretically described by an infinite population model with the help of a deterministic dynamical system model by which the stochastic trajectory of a population can be characterized using a determinis...

We propose an approach in solving a parameter estimation problem delay differential equations (DDE). By taking the interpolation of data set, is posed as minimization using L2–norm. use heuristic algorithm called Genetic Algorithm with multi-parent crossover to numerically solve optimization problem. consider four DDE models biological systems discrete and continuous test our method.

this paper introduces a technique for controlling a class of uncertain chaotic systems using an adaptive fuzzy proportional-integrator-derivative (pid) controller with h∞ tracking performance. the purpose of this work is to achieve optimal tracking performance of the controller using backtracking search algorithm (bsa). bsa, which is a novel heuristic algorithm, has an easy structure with singl...

Genetic Algorithms (GAs) are well-known heuristic algorithms and have been widely applied to solve combinatorial problems. Edge recombination is one of the famous crossovers designed for GAs to solve combinatorial problems. The essence of edge recombination is to achieve maximal inheritance from parental edges. This paper presents two strategies to improve edge recombination. First, we encourag...

As we know rice is the main food in China. In recent years, the rapid development of agricultural decision support system provides new management methods for rice production. GA has been regarded as an effective analytic tool and stochastic search technique to solve large and complicated problems. However, traditional GA-based decision support system does not always result in good solutions and...