scholarly journals Adaptation to Software of Epigenetic Algorithm

2018 ◽  
pp. 26-30
Author(s):  
Serdar Birogul
Keyword(s):  
Genetic Algorithm ◽  
Solution Algorithm ◽  
Research Topic ◽  
Epigenetic Factors ◽  
Successful Solution ◽  
Short Time

Genetic algorithm has been used in several researches to be a successful solution algorithm. In this study, Concept of Epigenetic is presented different perspective for GA to find a better solutions and results in short time. Randomness is a matter of GA. Adaptation of the epigenetic to GA design, which is one of the research topic that have been seriously investigated in the field of medicine and biology Reduces this randomness.randomization of Epigenetic crossing and mutation shows that the procession is not lucky happened. In addition crossover and change operators in the classical GA, epicrossover and epimutation operators in EGA software,shows how epigenetic factors work and how epiheritance is possible.

scholarly journals Synthesis of Metal Organic Frameworks by Ball-Milling

Crystals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Cheng-An Tao ◽  
Jian-Fang Wang
Keyword(s):  
Ball Milling ◽  
Large Scale ◽  
Raw Materials ◽  
Metal Organic Frameworks ◽  
Research Topic ◽  
Mass Ratio ◽  
Time Consumption ◽  
Metal Organic ◽  
Short Time ◽  
Made In

Metal-organic frameworks (MOFs) have been used in adsorption, separation, catalysis, sensing, photo/electro/magnetics, and biomedical fields because of their unique periodic pore structure and excellent properties and have become a hot research topic in recent years. Ball milling is a method of small pollution, short time-consumption, and large-scale synthesis of MOFs. In recent years, many important advances have been made. In this paper, the influencing factors of MOFs synthesized by grinding were reviewed systematically from four aspects: auxiliary additives, metal sources, organic linkers, and reaction specific conditions (such as frequency, reaction time, and mass ratio of ball and raw materials). The prospect for the future development of the synthesis of MOFs by grinding was proposed.

Algorithmic music generation by harmony recombination with genetic algorithm

2021 ◽  
pp. 1-13
Author(s):  
Omar Lopez-Rincon ◽  
Oleg Starostenko ◽  
Alejandro Lopez-Rincon
Keyword(s):  
Genetic Algorithm ◽  
New Music ◽  
Novel Approach ◽  
Time Patterns ◽  
Algorithmic Music ◽  
Long Time ◽  
Music Generation ◽  
Music Piece ◽  
Short Time ◽  
Algorithmic Music Composition

Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness.

scholarly journals An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

2013 ◽  
Vol 23 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Fei Yan ◽  
Mahjoub Dridi ◽  
Abdellah El Moudni
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Control Methods ◽  
Traffic Condition ◽  
Sequencing Problem ◽  
Genetic Algorithm Approach ◽  
Short Time ◽  
Intersection Management

This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.

scholarly journals Development Of A New Genetic Algorithm For Solving Capacitated Vehicle Routing Problem In Short Time

2019 ◽  
Vol 8 (11) ◽  
pp. 903-907
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Benchmark Instances ◽  
Full Capacity ◽  
Short Time ◽  
Number Of Customers ◽  
Capacitated Vehicle

In this paper a new genetic algorithm is developed for solving capacitated vehicle routing problem (CVRP) in situations where demand is unknown till the beginning of the trip. In these situations it is not possible normal metaheuristics due to time constraints. The new method proposed uses a new genetic algorithm based on modified sweep algorithm that produces a solution with the least number of vehicles, in a relatively short amount of time. The objective of having least number of vehicles is achieved by loading the vehicles nearly to their full capacity, by skipping some of the customers. The reduction in processing time is achieved by restricting the number of chromosomes to just one. This method is tested on 3 sets of standard benchmark instances (A, M, and G) found in the literature. The results are compared with the results from normal metaheuristic method which produces reasonably accurate results. The results indicate that whenever the number of customers and number of vehicles are large the new genetic algorithm provides a much better solution in terms of the CPU time without much increase in total distance traveled. If time permits the output from this method can be further improved by using normal established metaheuristics to get better solution

scholarly journals Penerapan Algoritma Genetika Untuk Optimasi Penjadwalan pada MTS Negeri 1 Pangkalpinang

2020 ◽  
Vol 9 (3) ◽  
pp. 435-441
Author(s):  
Delpiah Wahyuningsih ◽  
Ellya Helmud
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Important Thing ◽  
Long Time ◽  
Time Required ◽  
Short Time

Scheduling is a very important thing to do at school. The schedule, which is still being carried out manually at MTS Negeri 1 Pangkalpinang, requires time to manage teacher slots, classes, subjects, and times where in MTS teacher hours have been determined by the department of religion so that it takes quite a long time to process the formation of the schedule. This study aims to utilize genetic algorithms in optimizing scheduling in a short time. The genetic algorithm is an algorithm that is effective in dealing with scheduling. The results of data testing were carried out with 15, 20, 25 and 30 subjects. Testing with 15 subjects took 19.56 seconds to form a schedule and there were no conflicting schedules, while with 20 data subjects the time to process the schedule formation took 42.15 seconds, 25 data with 94.07 seconds and 30 data with time 471.60. The average time required to process the data is 156.845 seconds.

scholarly journals Steel logistics park layout based on market basket analysis and accessibility

2021 ◽  
Vol 336 ◽  
pp. 09022
Author(s):  
Qianqian Yang ◽  
Lu Qin
Keyword(s):  
Genetic Algorithm ◽  
Maximum Degree ◽  
Solution Algorithm ◽  
Market Basket Analysis ◽  
Retention Strategy ◽  
Logistics Park ◽  
Market Basket ◽  
Before And After ◽  
Space Layout ◽  
Staying Time

In this study, we used market basket analysis and land accessibility for the layout of MG Steel Logistics Park. Due to the unreasonable cargo space layout, the outbound trucks in the MG Steel Logistics Park detour and transfer, which caused congestion. Using market basket analysis, this research took outbound trucks as the research object to measure the degree of correlation between cargo locations. Then, we measured the convenience of cargo locations by the accessibility and matched it with the steel outbound frequency. We established a model and got the layout allocation plan with the maximum degree of relevance and the minimum difference in cargo-location matching as the target. In the solution algorithm, we use roulette method and the elite retention strategy to improve the genetic algorithm. Finally, we compared the results before and after optimization. We found that the number of truck transfers was reduced by 33%, and the truck staying time was reduced by 21%.

A Game Theory Approach for Multiple Design Tasks Schedule

2010 ◽  
Vol 26-28 ◽  
pp. 163-166
Author(s):  
Guo Hai Zhang ◽  
Guang Hui Zhou ◽  
Xue Qun Su
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Game Theory ◽  
Solution Algorithm ◽  
Optimal Scheduling ◽  
Theory Approach ◽  
Scheduling Problem ◽  
Tasks Scheduling ◽  
Numerical Case Study

This paper presents a new kind of scheduling solution for multiple design tasks in networked developing environments. The main contributions of this study can be focused on three points: The first is to distinguish the concepts and contents of the task scheduling in the networked developing environments. The second is to construct a game-theory mathematical model to deal with this new multiple design tasks scheduling problem. In the presented mathematical model, the players, strategies and payoff are given separately. Therefore, obtaining the optimal scheduling results is determined by the Nash equilibrium (NE) point of this game. In order to find the NE point, a genetic algorithm (GA)-based solution algorithm to solve this mathematical model is proposed. Finally, a numerical case study is presented to demonstrate the feasibility of the methods.

scholarly journals Decision-Making Approach of Two-Sided Matching of Supply and Demand of Logistics Service Based on the Uncertain Preference Ordinal

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Na Wang ◽  
Yinzhen Li ◽  
Cunjie Dai ◽  
Francesco Zammori
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Model ◽  
Supply And Demand ◽  
Solution Algorithm ◽  
Demand Side ◽  
Interval Numbers ◽  
Ordinal Information ◽  
Logistics Service ◽  
Nondominated Sorting

Aiming at the problem of matching logistics service supply and demand, this paper proposes a two-sided matching decision model of logistics service supply and demand based on the uncertain preference ordinal. In this model, the uncertain preference ordinal information is first expressed by the interval numbers of the logistics service supply and demand, and it is converted into the satisfaction degree of supply and demand matching uncertainty expressed by the interval number. Then, a multiobjective optimal matching model is constructed based on the largest overall satisfaction of the logistics service supply and demand side and the smallest satisfaction variance of the supply- and demand-side individual, and the multiobjective solution algorithm is designed based on nondominated sorting genetic algorithm-III (NSGA-III). Interval numbers are used to sort the matching results to obtain the approximately optimal two-sided matching scheme. Finally, this paper verifies the correctness of the model and validity of the algorithm.

Sign in / Sign up

Export Citation Format

Share Document