scholarly journals A genetic algorithm for composing music

2010 ◽  
Vol 20 (1) ◽  
pp. 157-177 ◽  
Author(s):  
Dragan Matic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Material ◽  
Premature Convergence ◽  
Initial Population ◽  
Musical Score ◽  
Genetic Operators ◽  
Suboptimal Solutions ◽  
Starting Solutions

In this paper, a genetic algorithm for making music compositions is presented. Position based representation of rhythm and relative representation of pitches, based on measuring relation from starting pitch, allow for a flexible and robust way for encoding music compositions. This approach includes a pre-defined rhythm applied to initial population, giving good starting solutions. Modified genetic operators enable significantly changing scheduling of pitches and breaks, which can restore good genetic material and prevent from premature convergence in bad suboptimal solutions. Beside main principles of the algorithm and methodology of development, in this paper the analysis of solutions in general is also presented, as well as the analysis of the obtained solutions in relation to the key parameters. Some solutions are presented in the musical score.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

Application of Improved Genetic Algorithm to Traffic Equipment’s Rush-Repair in the Wartime

2012 ◽  
Vol 605-607 ◽  
pp. 49-52
Author(s):  
Geng Sheng Wu ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Initial Population ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Assignment Model ◽  
Practical Engineering ◽  
Poor Stability

Traffic equipment’s rush-repairs in the wartime optimal assignment model was established. Combining the features of Job-shop scheduling problems, described the complexity of this problem. In order to find global optimal results efficiently, traditional GAs were improved and used for study of this problem. Though genetic algorithm, as an effective global search method, had been used in many problems, it had the disadvantages of slow convergence and poor stability in practical engineering. In order to overcome these problems, an improved genetic algorithm was proposed in terms of creation of the initial population, genetic operators, etc. At the end, the steps to solve the optimal model were put forward. With this model we had obtained ideal results. This shows that the method can offer a scientific and effective support for a decision maker in command automation of the traffic equipment’s rush-repairs in battlefield.

Network Coding Optimization Method Research Based on Genetic Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2059-2062
Author(s):  
Hong Yan Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Optimization Method ◽  
Premature Convergence ◽  
Initial Population ◽  
Running Time ◽  
Coding Scheme ◽  
Algorithm Search

Network coding optimization method research based on genetic algorithm applies network coding technology in monophyletic multicast network. After reaching network multicast rate, find link coding scheme which makes the minimum of the total number of network coding. Moreover, it makes the analysis and improvement for general genetic algorithm’s defects in network coding link optimization such as rare individual successful decoded by randomly generated initial population strategies, reduced algorithm search ability, premature convergence of genetic algorithm and long algorithm running time.

Genetic Algorithm for Optimizing Traveling Salesman Problems with Time Windows (TSP-TW)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Juwairiah Juwairiah ◽  
Dicky Pratama ◽  
Heru Cahya Rustamaji ◽  
Herry Sofyan ◽  
Dessyanto Boedi Prasetyo
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Time Windows ◽  
Time Variable ◽  
Traveling Salesman ◽  
Initial Population ◽  
Genetic Operators ◽  
Traveling Salesman Problems ◽  
Exchange Method ◽  
The Traveling Salesman Problem

The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.

scholarly journals The concept of genetic programming in organizing internal transport processes

2015 ◽  
Vol 34 (2) ◽  
pp. 61-74 ◽  
Author(s):  
Konrad Lewczuk
Keyword(s):  
Genetic Algorithm ◽  
Transport Process ◽  
Transport Processes ◽  
Initial Population ◽  
Genetic Operators ◽  
Optimization Task ◽  
Support Organization ◽  
Scope Of Application ◽  
Feasible Solutions ◽  
Internal Transport

The paper presents proposition of using genetic algorithm to support organization of internal transport processes in logistics facilities. The organization of internal transport can be done through solving optimization task of scheduling internal transport process with allocation of human resources and equipment to the tasks. Internal transport process was defined and discussed as an object of organization. Precise methods of solving proposed optimization task were unable to give useful solutions according to the computational complexity of the problem, so heuristic genetic algorithm was proposed. The possible structures of chromosome representing feasible solutions, methods of generating initial population, base genetic operators: selection and inheritance, crossover, mutation and fixing of individuals were described. The main implementation difficulties, computational experiments and the scope of application of the algorithm were discussed.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

scholarly journals A GENETIC APPROACH FOR LONG TERM VIRTUAL ORGANIZATION DISTRIBUTION

2011 ◽  
Vol 20 (02) ◽  
pp. 271-295 ◽  
Author(s):  
VÍCTOR SÁNCHEZ-ANGUIX ◽  
SOLEDAD VALERO ◽  
ANA GARCÍA-FORNES
Keyword(s):  
Genetic Algorithm ◽  
Virtual Organization ◽  
Genetic Material ◽  
Multi Agent System ◽  
Crossover Operator ◽  
Agent Based ◽  
Multi Agent ◽  
Global Goal ◽  
Computational Resources

An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

scholarly journals Research on Logistics Distribution Path Planning Based on Genetic Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032013
Author(s):  
Shaokun Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulation Experiment ◽  
Time Window ◽  
Planning Model ◽  
Genetic Operators ◽  
Intelligent Algorithm ◽  
Total Cost ◽  
Logistics Distribution ◽  
Survey Results ◽  
Distribution Cost

Abstract In this paper, SF express company Jinzhou Guta District Pinganli business point as an example, to investigate its distribution, statistical analysis of the survey results, summed up the problems in logistics and distribution. Through the systematic study of the problem, a planning model with time window and with the objective of minimizing the total cost of distribution is established. At the same time, an intelligent algorithm for distribution path optimization - Genetic Algorithm (GA) is designed. Genetic algorithm is used to design chromosome coding methods and genetic operators for solving the planning model with the objective of minimizing the total cost of distribution. Finally, the simulation experiment is carried out. MATLAB software is used to solve the distribution route and the total driving distance of vehicles, and the distribution route with the goal of minimizing the total distribution cost is obtained.

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