The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized...
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...
To find the best polarity of large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits, this paper proposes a new Adaptive Simulated Annealing Genetic Algorithm (ASAGA) which can effectively find out the best polarity. Genetic Algorithm (GA) has outstanding global searching ability but easily falls into the local optimum, while the Simulated Annealing Algorithm (SAA) is expert in local searching...
This paper presents novel approach for optimal distribution network reconfiguration using the combination of cycle-break algorithm and genetic algorithms. Significant improvements are introduced in the phases of initial population generation as well as other general operations inside genetic algorithm. These improvements lead to better convergence rate and computational time reduction. Even though...
In most global optimization problems, finding a global optimum point in the whole multi-dimensional search space implies a high computational burden. We present a new approach called subdividing labeling genetic algorithm (SLGA) for continuous nonlinear optimization problems. SLGA applies mutation and crossover operators on a subdivided search space where an integer label is defined on a polytope...
In view of the problem of premature convergence of simple genetic algorithm, a multi population genetic algorithm for traffic assignment problems was proposed, multiple populations are introduced and search simultaneous, coevolution through populations are implemented, use immigration operator to exchange information, artificial selection operator to keep best individuals of every generation, and...
For larger system, solution space increases exponentially with the number of time periods and units in the system, therefore the computation time becomes impractical. This paper presents an improved two layer approach for solution for large systems. The first layer generate constraints satisfied high fitness population (HFP) and the second layer is incorporated with a GA algorithm for solving the...
For larger system, solution space increases exponentially with the number of time periods and units in the system, therefore the computation time becomes impractical. This paper presents an improved two layer approach for solution for large systems. The first layer generate constraints satisfied high fitness population (HFP) and the second layer is incorporated with a GA algorithm for solving the...
In this paper, Genetic Algorithm (GA) is used to solve the Unit Commitment (UC) Problem. Unit commitment problem was formulated with consideration of up & down time, startup cost (Hot & Cold start), and production cost. Unit commitment schedule as well as economic dispatch is obtained to obtain total cost of generation. Problem specific operators are used in the algorithm to improve the quality...
This paper conducts a comparative study between an improved variants of genetic algorithm (GA) and a swarm intelligence algorithm (SIA), which are the Dual population Genetic Algorithm (DPGA) and Artificial Bee Colony (ABC) Algorithm. DPGA is a multi-population genetic algorithm (MPGA) that implements two population such as the main population and a complementary population. Since the added population...
Based on the characteristic of autonomous underwater vehicle path planning, the method of path planning was analyzed by genetic algorithm. Firstly, by means of grid, plan space was modeled into two markers. Best path was searched by genetic algorithm. Method of giving birth to initial groups was improved. Sufficiency function of path planning was given. Chamfer operator in genetic algorithm was imported...
This paper proposes a new solution for Traveling Salesman Problem (TSP) using genetic algorithm. A combinational crossover technique is employed in the search for optimal or near-optimal TSP solutions. It is based upon chromosomes that utilise the concept of heritable building blocks. Moreover, generation of a single offspring, rather than two, per pair of parents, allows the system to generate high...
The paper presents a novel hybrid searching COMBI GMDH-GA algorithm with GA used to discover model of optimal structure quickly because of avoiding exhaustive search. The obtained experimental results demonstrate that this algorithm performs well when solving inductive modelling tasks, both artificial and real-world.
This paper proposes a hybrid algorithm based on the genetic algorithm (GA) and the evolution strategy (ES) for the electromagnetic optimization problem. The GA is not good enough at times in searching the optimal solution from the view point of the convergence speed and the solution quality, while the ES has the risk of being trapped in a local minimum. The hybrid algorithm is composed of GA and ES...
Some of the engineering applications warrant the solution of Graph Coloring Problem. This paper investigates a new genetic procedure using divide and conquer strategy on some of the intermediate (100 ≤ n ≤ 500) and large scale benchmark graphs (n ≥ 500) to obtain the near optimal chromatic number. Finding the chromatic number is an NP-hard and combinatorial optimization problem. The divide & conquer...
Feature subset selection is an important research branch in the field of pattern recognition. Due to the traditional feature selection algorithms do not take into account the feature updating case, the paper analyzes the relationship between dataset and features, proposes a new feature activity measurement that is used to determine the influence among different features on some certain conditions...
FCM is sensitive to initialization and tends to result in local minimum in iterations. This paper studies the crossover and mutation probability of genetic algorithm and presents a new crossover and mutation probability. The proposed clustering scheme based on genetic algorithm and fuzzy c-means takes full advantage of the global optimization of genetic algorithm and the local search ability of FCM...
The study on the programmed cell death shows that the death of cells is controlled by genes. Based on this theory, an evolutionary algorithm is simulated by introducing control genes operators in the genetic algorithm to optima the traditional genetic algorithm. Markov chain is used to analyze the convergence of programmed cell death algorithm. It can be proved that this new algorithm can converge...
Solving puzzles based on number-sum or ordered matrices is an NP-hard problem that requires considerable computational effort. Prime examples of these are the games Sudoku and Kakuro. Kakuro relies on number sequences that must sum to a number indicator shown on the puzzle. Sudoku requires that numbers be listed in an implicit sequence in blocks and rows. Both puzzles require exclusivity of the numbers...
This paper presents a new robust methodology for solving radial distribution system reconfiguration (DSR) problem based on the concept of cooperative multi-thread strategy and hybrid meta-heuristics. The parallel cooperative meta-heuristics (PCMH) method deploys multiple concurrent explorations of the solution space using genetic algorithm (GA), particle swarm optimization (PSO) and ant colony system...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.