In this paper, an Evolutionary Qualitative Model Learning Framework (EQML) is proposed and tested by learning the qualitative metabolic models under the condition of incomplete knowledge. JMorven, a fuzzy qualitative reasoning engine, is slightly modified and integrated into the framework as a sub module to represent and verify the learnt models. Three metabolic compartment models are tested by...

The aim of this Master’s thesis is the investigation of schedule qualities by extending the HEFT algorithm and a Genetic Algorithm to contention-aware scheduling heuristics. Both algorithms are modified in terms of transfer times for simultaneously data connections inside the cluster to explore potential advantages of the GA on the one hand, and the increasing complexity of the problem on the o...

An efficient assignment and scheduling of tasks is one of the key elements in effective utilization of heterogeneous multiprocessor systems. The task scheduling problem has been proven to be NP-hard is the reason why we used meta-heuristic methods for finding a suboptimal schedule. In this paper we proposed a new approach using TRIZ (specially 40 inventive principles). The basic idea of thi...

The aim of this paper is to clearly demonstrate the importance of finding a good balance between genetic search and local search in the implementation of hybrid evolutionary multicriterion optimization (EMO) algorithms. We first modify the local search part of an existing multi-objective genetic local search (MOGLS) algorithm. In the modified MOGLS algorithm, the computation time spent by local...

The aim of this paper is to clearly demonstrate the importance of finding a good balance between genetic search and local search in the implementation of hybrid evolutionary multi-criterion optimization (EMO) algorithms. We first modify the local search part of an existing multi-objective genetic local search (MOGLS) algorithm. In the modified MOGLS algorithm, the computation time spent by loca...

In the field of vehicle path planning, traditional intelligent optimization algorithms have disadvantages slow convergence, poor stability and a tendency to fall into local extremes. Therefore, gradient statistical mutation quantum genetic algorithm (GSM-QGA) is proposed. Based on dynamic rotation angle adjustment by chromosome fitness value, gate strategy improved introducing idea descent. Acc...

This paper presents an approach for portfolio selection using evolutionary programming as a tool for optimization. The goal is to find the mix of stocks that minimize risk expressed as standard deviation for a certain expected return. Two alternatives approaches are developed (Hillclimbing and Random) to measure the performance of the modified genetic algorithm. Key-Words: Evolutionary Computin...

In this paper posibility of design and optimization of combinational digital circuits using modified evolutionary algorithm is presented. Modification of evolutionary algorithm depends on introduction of multilayer chromosomes and genetic operators operating on them. Design results for four combinational circuits obtained using this method are compared with described in literature methods: Karn...

Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method tha...