Strategic Team AI Path Plans: Probabilistic Pathfinding

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

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OntoQuery-An Evolutionary Approach for Query Formation and Information Retrieval

Mobile Commerce Applications ◽

10.4018/978-1-59140-182-7.ch007 ◽

2011 ◽

pp. 140-160

Author(s):

Sheng-Uei Guan ◽

Chang Ching Chng ◽

Fangming Zhu

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Information Retrieval ◽

Query Optimization ◽

Formation Process ◽

Relevance Feedback ◽

Fitness Function ◽

Optimization Techniques ◽

Retrieval Scheme

This chapter proposes the establishment of OntoQuery in an m-commerce agent framework. OntoQuery represents a new query formation approach that combines the usage of ontology and keywords. This approach takes advantage of the tree pathway structure in ontology to form queries visually and efficiently. Also, it uses keywords to complete the query formation process more efficiently. Present query optimization techniques like relevance feedback use expensive iterations. The proposed information retrieval scheme focuses on using genetic algorithms to improve computational effectiveness. Mutations are done on queries formed in the earlier part by replacing terms with synonyms. Query optimization techniques used include query restructuring by logical terms and numerical constraints replacement. Also, the fitness function of the genetic algorithm is defined by three elements, number of documents retrieved, quality of documents, and correlation of queries. The number and quality of documents retrieved give the basic strength of a mutated query.

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Genetic algorithm of optimizing the qualification of programmer teams

«System analysis and applied information science» ◽

10.21122/2309-4923-2020-4-31-38 ◽

2021 ◽

pp. 31-38

Author(s):

A. A. Prihozhy ◽

A. M. Zhdanouski

Keyword(s):

Genetic Algorithm ◽

Fitness Function ◽

Combinatorial Problem ◽

Point Of View ◽

Experimental Results ◽

Optimization Process ◽

High Quality

The partitioning a set of professional programmers into a set of teams when a programming project specifies requirements to the competency in various programming technologies and tools is a hard combinatorial problem. The paper proposes a genetic algorithm, which is capable of finding competitive and high-quality partitioning solutions in acceptable runtime. The algorithm introduces chromosomes in such a way as to assign each programmer to a team, define the team staff and easily reconstruct the teams during optimization process. A fitness function characterizes each chromosome with respect to the quality of the programmers partitioning. It accounts for the average qualification of teams and the qualification of team best representatives on each of the technologies. The function recognizes the teams that meet all constraints on the project and are workable from this point of view. It is also capable of recognizing the teams that do not meet the constraints and are unworkable. The algorithm defines the genetic operations of selection, crossing and mutation in such a way as to move programmers from unworkable to workable teams, to increase the number of workable teams, to ex-change programmers among workable teams, to increase the competency of every workable team, and thus to maximize the teams overall qualification. Experimental results obtained on a set of programmers graduated from Belarus universities show the capability of the genetic algorithm to find good partitioning solutions, maximize the teams’ competency and minimize the number of unemployed programmers.

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Investigation of the Possibility of Applying a Genetic Algorithm for Electroacoustics Problems

Microsystems Electronics and Acoustics ◽

10.20535/2523-4455.mea.227793 ◽

2021 ◽

Vol 26 (1) ◽

Author(s):

Artem Dmytrovych Zubkov ◽

Denys Dmytrovych Volkov ◽

Vitalii Semenovych Didkovkyi

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Classical Method ◽

Fitness Function ◽

Added Mass ◽

Mathematical Representation ◽

Advantages And Disadvantages ◽

Electroacoustic Transducer ◽

Representation Of Solutions

This paper considers the adaptation and application of a genetic algorithm to find the parameters of the electrodynamic transducer model. The advantages and disadvantages of this method in comparison with the classical method of identification using added mass are considered. The derivation of the suitability function for estimating the identified parameters is presented, which can also be used to identify other types of electroacoustic transducers. The theory underlying genetic algorithms has been examined and shown how genetic algorithms work by assembling the best solutions from small structural elements with excellent qualities. Next, the differences between genetic and traditional algorithms were analyzed, including population population support and the use of genetic representation of solutions. After that, the strengths of genetic algorithms were described, including the possibility of global optimization and applicability to problems with complex mathematical representation or without representation at all, and noise resistance. Disadvantages were also highlighted: the need for special definitions and settings of hyperparameters, the danger of premature convergence. In conclusion, the situations when the use of genetic algorithms are listed This algorithm is not tied to a specific engineering or scientific field, which makes it universal, it is equally used in genetics and computer science. The parameters were determined using a genetic algorithm and compared with the more classical method of added mass for acoustics. The comparative table in the work illustrates the high accuracy of the genetic algorithm in comparison with the method of added mass. During the work on the practical part, also to improve the behavior of the model at frequencies higher than the resonant, it was decided to complicate the model of the electrical subsystem of the tranducer and introduce additional parameters: parallel resistance and parallel inductance. As a result, the complicated model began to correspond better to the measured values in the entire frequency domain, and is therefore more accurate. This is an example of the convenience of using a genetic algorithm in the transition from identification of one model with specific parameters to another. The results of this work prove that the use of a genetic algorithm is appropriate for solving electroacoustic problems because its application allows to quickly experiment and identify more complex models for which the added mass method can not be applied. Also, in the future, genetic algorithm can be used to identify transducer models of in time domain, for example, nonlinear models of electrodynamic transducers or models in a state space, which is the subject of future research.This paper considers the adaptation and application of a genetic algorithm to find the parameters of the electrodynamic transducer model. The advantages and disadvantages of this method in comparison with the classical method of identification using added mass and the method of parameter selection BL are considered. The derivation of the fitness function for assessing the quality of the identified parameters is presented, which can also be used to identify other types of electroacoustic transducers. The directly measured values for the application of the algorithm are the voltage at the terminals of the converter, the current through the coil of the converter and the displacement of the moving part of the converter. The undoubted advantage of the genetic algorithm compared to classical identification methods is its versatility and the ability to quickly adapt and configure for research and experimentation with different models and different types of transducers used in acoustics. This article describes the adaptation and application of a genetic algorithm to find parameters of an electrodynamic transducer model. The advantages and disadvantages of this method in comparison with the classical identification method using added mass are considered. The derivation of the fitness function for assessing quality of the identified parameters is presented, which can also be used to identify other types of electroacoustic transducer models.

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Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽

10.3390/en14010115 ◽

2020 ◽

Vol 14 (1) ◽

pp. 115

Author(s):

Andriy Chaban ◽

Marek Lis ◽

Andrzej Szafraniec ◽

Radoslaw Jedynak

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Fitness Function ◽

Least Square ◽

Distributed Parameter ◽

Parameter Method ◽

Analytical Mechanics ◽

Parameter System ◽

Rotational Systems ◽

Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

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Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.33.23516 ◽

2018 ◽

Vol 7 (4.33) ◽

pp. 130

Author(s):

Atiqa Zukreena Zakuan ◽

Shuzlina Abdul-Rahman ◽

Hamidah Jantan ◽

. .

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Succession Planning ◽

Fitness Function ◽

Academic Staff ◽

Fitness Value ◽

Selection Decision ◽

The University ◽

Hierarchy Process

Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.

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Application of the genetic algorithm to the task of compiling the curriculum

Problems of applied mathematics and mathematic modeling ◽

10.15421/321820 ◽

2018 ◽

Author(s):

V. A. Turchina ◽

D. O. Tanasienko

Keyword(s):

Higher Education ◽

Genetic Algorithm ◽

Genetic Algorithms ◽

Necessary Conditions ◽

Fitness Function ◽

Educational Process ◽

Heuristic Methods ◽

Educational Groups ◽

The Individual ◽

Negative Property

One of the main tasks in organizing the educational process in higher education is the drawing up of a schedule of classes. It reflects the weekly student and faculty load. At the same time, when compiling, there are a number of necessary conditions and a number of desirable. The paper considers seven required and four desirable conditions. In this paper, one of the well-known approaches that can be used in drawing up a curriculum is consid-ered. The proposed scheme of the genetic algorithm, the result of which is to obtain an approximate solution to the problem of scheduling with the need to further improve it by other heuristic methods. To solve the problem, an island model of the genetic algorithm was selected and its advantages were considered. In the paper, the author's own structure of the individual, which includes chromosomes in the form of educational groups and genes as a lesson at a certain time, is presented and justified. The author presents his own implementations of the genetic algorithms. During the work, many variants of operators were tested, but they were rejected due to their inefficiency. The biggest problem was to maintain the consistency of information encoded in chromosomes. Also, two post-steps were added: to try to reduce the number of teacher conflict conflicts and to normalize the schedule - to remove windows from the schedule. The fitness function is calculated according to the following principles: if some desired or desired property is present in the individual, then a certain number is deducted from the individual's assessment, if there is a negative property, then a certain number is added to the assessment. Each criterion has its weight, so the size of the fine or rewards may be different. In this work, fines were charged for non-fulfillment of mandatory conditions, and rewards for fulfilling the desired

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Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

International Journal of Professional Business Review ◽

10.26668/businessreview/2018.v3i2.49 ◽

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.

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PENENTUAN PARAMETER LEARNING RATE SELAMA PEMBELAJARAN JARINGAN SYARAF TIRUAN BACKPROPAGATION MENGGUNAKAN ALGORITMA GENETIKA

Jurnal Teknologi Informasi Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ◽

10.47111/jti.v14i2.1141 ◽

2020 ◽

Vol 14 (2) ◽

pp. 202-212

Author(s):

Ade chandra Saputra

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Genetic Algorithms ◽

Fitness Function ◽

Learning Rate ◽

Local Minima ◽

Rate Parameter ◽

Neural Network Learning ◽

Value Of Learning ◽

Backpropagation Learning

One of the weakness in backpropagation Artificial neural network(ANN) is being stuck in local minima. Learning rate parameter is an important parameter in order to determine how fast the ANN Learning. This research is conducted to determine a method of finding the value of learning rate parameter using a genetic algorithm when neural network learning stops and the error value is not reached the stopping criteria or has not reached the convergence. Genetic algorithm is used to determine the value of learning rate used is based on the calculation of the fitness function with the input of the ANN weights, gradient error, and bias. The calculation of the fitness function will produce an error value of each learning rate which represents each candidate solutions or individual genetic algorithms. Each individual is determined by sum of squared error value. One with the smallest SSE is the best individual. The value of learning rate has chosen will be used to continue learning so that it can lower the value of the error or speed up the learning towards convergence. The final result of this study is to provide a new solution to resolve the problem in the backpropagation learning that often have problems in determining the learning parameters. These results indicate that the method of genetic algorithms can provide a solution for backpropagation learning in order to decrease the value of SSE when learning of ANN has been static in large error conditions, or stuck in local minima

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Genetic Algorithms for Decision-Making in Cognitive Radio Networks

Self-Organization and Green Applications in Cognitive Radio Networks ◽

10.4018/978-1-4666-2812-0.ch012 ◽

2013 ◽

pp. 247-263

Author(s):

Tommy Hult ◽

Abbas Mohammed

Keyword(s):

Genetic Algorithm ◽

Decision Making ◽

Genetic Algorithms ◽

Cognitive Radio ◽

Search Space ◽

Radio Spectrum ◽

Dynamic Decision Making ◽

Optimal Point ◽

Optimization Search

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users dynamically allocate the empty frequencies within the licensed frequency band according to their requested QoS (Quality of Service) specifications. This dynamic decision-making is a multi-criteria optimization problem, which the authors propose to solve using a genetic algorithm. Genetic algorithms traverse the optimization search space using a multitude of parallel solutions and choosing the solution that has the best overall fit to the criteria. Due to this parallelism, the genetic algorithm is less likely than traditional algorithms to get caught at a local optimal point.

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