This paper describes research on the use of a multiobjective genetic algorithm (GA) to optimize prescriptive treatment plans for forest management. The algorithm is novel, in that (1) the plans generated by the algorithm are highly specific, stating precisely when and where treatments are to be applied; and (2) logical rules and inference engines developed for a decision support system are used...

This paper presents a production-inventory model for deteriorating items with stock-dependent demand under inflation in a random planning horizon. The supplier offers the retailer fully permissible delay in payment. It is assumed that the time horizon of the business period is random in nature and follows exponential distribution with a known mean. Here learning effect is also introduced for th...

In this paper we propose a modification to Genetic Algorithm which we call as Retrievable Genetic Algorithm (Ret-GA) and use it to solve a given Sudoku puzzle. Although a number of approaches exist for solving a given Sudoku puzzle, very few papers with heuristic approaches are available for obtaining its solution. In this work, the fitness function is modeled in a new way considering puzzle-ch...

Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water moves through the soil. On other hand, its measurement difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for estimation. Despite significant development over years, PTFs showed poor performance in predicting Ks. Using Genetic Algorithm (GA), two hybrid Mach...

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...

In actual complicated optimization problems, it is often difficult to find a global optimum solution in admissible computing time. Therefore, in industrial problems, we should find quasi-optimum solution in admissible computing time. In that case, evolutionary computations (ECs) are very attractive. There are several algorithms in the EC family; genetic algorithm (GA), evolutionary strategy (ES...

In this paper we present an enhanced evolutionary algorithm (EA) to solve computationally expensive design optimization problems. In this algorithm we integrate a genetic algorithm (GA) with a local search method to expedite convergence of the GA. We first use a GA to generate a population of data by evaluating real functions, then we construct computationally cheap surrogate models based on th...

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the...

This paper proposes that a parallel implementation of the genetic algorithm (GA) on the Internet will improve the algorithm’s performance. It is motivated by the possibility of aiding research into complex search and optimization problems that use the GA. Requirements and constraints regarding parallelization of the GA are identified. A parallel GA is developed for an ideal PRAM architecture an...