Operator-oriented genetic algorithm and its application to sliding block puzzle problem

VIII Prefaceusing different data structures for chromosome representation of individuals, and various "genetic" operators which operated on them. Because of my background in databases [122], [126], where the constraints playa central role, most evolution programs were developed for constrained problems.The idea of evolution programs (in the sense presented in this book), was conceived quite early [101], [133] and was supported later by a series of experiments. Despite the fact that evolution programs, in general, lack a strong theoretical background, the experimental results were more than encouraging: very often they performed much better than classical genetic algorithms, than commercial systems, and than other, best-known algorithms for a particular class of problems. Some other researchers, at different stages of their research, performed experiments which were perfect examples of the "evolution programming" technique -some of them are discussed in this volume. Chapter 8 presents a survey of evolution strategies -a technique developed in Germany by I. , [162] for parameter optimization problems. Many researchers investigated the properties of evolution systems for ordering problems, including the widely known, "traveling salesman problem" (Chapter 10). In Chapter 11 we present systems for a variety of problems including problems on graphs, scheduling, and partitioning. Chapter 12 describes the construction of an evolution program for inductive learning in attribute based spaces, developed by C. Janikow [97]. In the Conclusions, we briefly discuss evolution programs for generating LISP code to solve problems, developed by J. Koza [108], and present an idea for a new programming environment.

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“…[63], [185], [29], [36], etc.) to accommodate the problem to be solved, thus building efficient evolution programs.…”

Section: Anthony De Mello One Minute Wisdommentioning

confidence: 99%

Genetic Algorithms + Data Structures = Evolution Programs

Michalewicz

1992

Artificial Intelligence

4,597

631

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“…Suh and Lee (Suh et al, 1989) proposed GA to solve the sliding block case. Different from traditional methods, Suh's algorithm uses an operator-oriented representation scheme, where each move in the solution sequence is represented as ((x,y),o), where "(x,y)" denotes the location of its initial state and "o" represents as 0,1,2,3 denote moves (L, U, R, D).…”

Section: Sliding Blockmentioning

confidence: 99%

Using Genetic Algorithm to Solve Puzzle Games: A Review

Bukhori,

Felix,

Ali

2024

CNAHPC

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Puzzles have been recognized for their development as a popular form of entertainment due to their ability to intricately challenge the mind while engendering creativity in the player. The development of puzzle games has given rise to a new generation of puzzle games characterized by diverse sequences and different image variations. With the rapid development of puzzle games, we looked at solving approaches using Genetic Algorithms (GA). In this paper, we try to analyze several puzzle games such as Sliding Blocks, Sudoku, Tic-Tac-Toe, and Jigsaw that can be solved using GA. We found that 120 papers have examined the use of GA for puzzle games, and eliminated into 14 papers. We evaluated these 14 papers for each puzzle game we selected by comparing the chromosome representation, GA operator, GA parameters, and the results. Based on the discussion, the application of GA to solve puzzle games can be effectively executed with a high degree of accuracy. Puzzle games that use measurement methods such as Sliding Block, Sudoku, and Jigsaw run in a similar pattern. What is common to all of them is that the chromosomes are represented as matrices or arrays in all cases, and standard genetic operators such as selection, crossover, and mutation are used. The population size is large, often 1000 chromosomes, and parameters such as mutation rate are kept low, around 5%. On the other hand, the performance of GA for solving Tetris and Tic-Tac-Toe from each publication cannot be compared due to different measurement methods and metrics.

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Operator-oriented genetic algorithm and its application to sliding block puzzle problem

Cited by 2 publications

References 1 publication

Genetic Algorithms + Data Structures = Evolution Programs

Genetic Algorithms + Data Structures = Evolution Programs

Using Genetic Algorithm to Solve Puzzle Games: A Review

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