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We discuss several ways to accelerate genetic algorithm-based instance selection, where the two objectives are a minimal number of training instances and maximal accuracy of the classifier (we use neural networks) on the test data. We discuss several ways to accelerate the process, but we especially focus on two parameters: fitness function and chromosome length reduction. We evaluate different fitness...
In this study, Vehicle Routing Problem with Time Windows (VRPTW) with known customer demands, a single depot and identical vehicles, is considered. Minimizing the total distance and the total waiting time of the vehicles are determined as objective functions for VRPTW which is capable to serve the customers in a prespecified time interval. A hybridized version of genetic algorithm which is a metaheuristic...
The paper presents a new mechanism to apply evolutionary complication of models in the previously introduced hybrid COMBI-GA sorting-out algorithm to find optimal model structure. The mechanism is based on generation of model structures using binomial random number generator with low probability and specific mutation operator. The presented experimental results demonstrate that this algorithm performs...
During last 10 years, internet usage network has spread at an unforeseen rate. Concurrently, many services including official transactions have been granted from the internet. By the way, it has become an area where internet users have access to apps and interact with each other over social media. People's internet usage habits have become a domain that can give information about the areas of the...
The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS patterns. One hundred twenty eight channel EEG signal was used in the experiments. The signal was recorded for 40 people, during the process of imagining right and left hand movements. Feature extraction was performed using frequency analysis (FFT) with the resolution of 1Hz. So the features were spectral lines associated...
The trend in the automotive industry is towards electric vehicles (EV), however, the industry will depend on gasoline engines for many years to come. There is also increased demand for the reduction of greenhouse gases. This work develops an adaptive model-based optimal control algorithm based on Sub-Structured Neural Network (SSANN), Multi-Objective Genetic Algorithms (GA), Multi-Objective Dragonfly...
Aiming at the problem of optimizing the structural of X-band standing wave accelerator with multi-parameter, a method for automatic cavities structure optimized and based on genetic algorithm was proposed in this paper. In this method accelerated structure model was built with multi-parameter, using MATLAB and electromagnetic stimulation software CST to work together to optimize multi-parameter structure,...
In recent years, process mining is important to discover process model from event logs; however the existing methods have not achieved good in overall fitness. In this context, this paper proposes a combination of the Evolutionary Tree Miner (ETM) and Simulated Annealing (SA). The ETM aims to reduce randomness of population so that it can improved the quality of individuals. SA aims to increase overall...
We present a strategy to improve an evolutionary process related to a hybridization of a genetic algorithm, strongly based on local search, for the weighted tardiness and Just-in-Time scheduling problems without idle time. The presented solutions represent a schedule in identical parallel machines that are indirectly encoded in a single sequence. An empirical analysis of these features is presented,...
Numerous genetic algorithms with Pareto-ranking were proposed for solving multiobjective optimisations (MOOs). Mainly, these algorithms compute the fitness values of the solutions via dominance analysis. For few conflicting objectives, dominance analysis is suitable for managing the partial sorting; however, this technique is not capable to handle other common requirements of MOOs, such as preserving...
In this paper, an adaptive genetic algorithm based on multi-population elite selection strategy is proposed. The multi-population elite selection strategy is used to preserve the optimal individuals of each group. Finally, these optimal individuals formed a population, and then use the improved adaptive genetic algorithm to finish the solution. By comparing the simulation experiments of TSP problem...
We take inspirations from nature very often in solving many complex scientific and day to day problems. Nature inspired computing is a branch of computer engineering deals with the development of algorithms simulating behaviors of natural species for solving complex problems not easily solvable by available computational models. Based on biological systems, various algorithms have been presented in...
In Non cooperative Game Theory, Nash Equilibrium can be computed by finding the best response strategy for each player. However this problem cannot be solved deterministically in polynomial time. For some finite games, there might be more than one pure strategy Game Equilibrium. In such cases, the most optimal set of solutions give the Game Equilibria. Evolutionary Algorithms and specifically Genetic...
The crucial objective of this paper is to design a hybrid model of the genetic algorithm for fuzzy extreme learning machine classifier (GA-FELM), which selects an optimal feature subset by using the multilevel parameter optimization technique. Feature subset selection is an important task in pattern classification and knowledge discovery problems. The generalization performance of the system is not...
This paper addresses the problem of Data Centers (DC) energy efficiency by proposing a proactive optimization technique to schedule the day-ahead DC operation to minimize the operational cost. The proactive optimization technique is formalized as a Mixed Integer Optimal Control Problem, known to be NP-hard. Because the time needed for solving this problem by some of the gradient-based solvers depends...
In this paper, a problem of specifying HIV-infection parameters and immune response using additional measurements of the concentrations of the T-lymphocytes, the free virus, and the immune effectors at fixed times for a mathematical model of HIV dynamics is investigated numerically. The problem of specifying the parameters of the mathematical model (an inverse problem) is reduced to a problem of minimizing...
To come over the limitations of Apriori algorithm and association rule mining algorithm based on Genetic Algorithm (GA), this paper proposed a new association rule mining algorithm based on the population-based incremental algorithm (PBIL), which is a kind of distribution estimation algorithms. The proposed association rule-mining algorithm keeps the advantages of GA mining association rules in coding...
Smart transportation is one of the essential components of smart cities that involves sensing traffic and pedestrians. Wireless Sensor Networks (WSN) have extensively been utilized over the years for sensing and data transfer in diverse structural deployments including mesh, ad hoc and hierarchical layouts. Several applications of WSN may involve placing the nodes in a linear topology, constituting...
Travelling Salesperson Problem being a classic combinatorial optimization problem is an interesting but a challenging problem to be solved. It falls under the class of NP-hard problem and becomes non-solvable for large data set by traditional methods like integer linear programming and branch and bound method, being the earlier popular approaches. Genetic Algorithm based solutions emerged as the most...
Feature selection is an extremely important matter in pattern recognition, particularly when a large set of features is available without knowledge about the discriminative information provided by each element. The key issue is to define a criterion in order to rank the features, discarding those features that are less relevant, redundant, or noisy. This depends on the particular task, the classifier...
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