Semidwarfism has been used extensively in row crops and horticulture to promote yield, reduce lodging, and improve harvest index, and it might have similar benefits for trees for short-rotation forestry or energy plantations, reclamation, phytoremediation, or other applications. We studied the effects of the dominant semidwarfism transgenes GA Insensitive (GAI) and Repressor of GAI-Like, which ...

In the recent past, genetic algorithm (GA) and evolutionary optimization scheme have become increasingly popular for the design of electromagnetic (EM) devices. However, the conventional GA suffers from computational drawback and parameter dependency when applied to a computationally expensive problem, such as practical EM optimization design. To overcome these issues, a hybrid optimization sch...

We present a new approach to the concrete planning (CP) shown to be a NP-hard problem [7]. This is the third stage of Web service composition (WSC) in the PlanICS framework [1]. The first two phases, namely an abstract planning and offer collecting, basing on an ontology, a user query, and a service registry provide data necessary for CP. A new hybrid algorithm (HSA), which combines Simulated A...

We have developed a hybrid optimization algorithm for inversion of gravity data using a binary formulation. The new algorithm utilizes the Genetic Algorithm (GA) as a global search tool, while implementing Quenched Simulated Annealing (QSA) intermittently for local search. The hybrid has significantly decreased computational cost over GA or Simulated Annealing (SA) alone and has allowed for suc...

A predictive long short-term memory (LSTM) model developed on a particular water quality dataset will only apply to the and may fail make an accurate prediction another dataset. This paper focuses improving LSTM tolerance by mitigating discrepancies in capability that arises when is applied different datasets. Two models are from datasets, Baffle Burnett, optimised using metaheuristic genetic a...

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

In this paper a new effective optimization algorithm called hybrid particle swarm optimizer with breeding and subpopulation is presented. This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, now in use for the optimization of electromagnetic structures, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social r...

A hybrid intelligent classiier is built for pattern classiication. It consists of a classiication and regression tree (CART), a genetic algorithm (GA) and a neural network (NN). CART extracts features of the patterns by setting up decision rules. Rule improvement by GA is explored. The rules act as a pre-processing layer of NN, a multi-class neural classiier, through which the most probable cla...

The success of an Artificial Neural Network (ANN) strongly depends on its training process. Gradient-based techniques have been satisfactorily used in the ANN training. However, in many cases, these algorithms are very slow and susceptible to the local minimum problem. In our work, we implemented a hybrid learning algorithm that integrates Genetic Algorithms(GAs) and the LevenbergMarquardt(LM) ...