Expert Systems with Applications: An International Journal
Solving Nonograms by combining relaxations
Pattern Recognition
A dynamic holding strategy in public transit systems with real-time information
Applied Intelligence
A hybrid approach to large-scale job shop scheduling
Applied Intelligence
Genetic algorithm for test pattern generator design
Applied Intelligence
A reasoning framework for solving nonograms
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Multi-objective Genetic Algorithms for grouping problems
Applied Intelligence
Optimization of Nonogram's Solver by Using an Efficient Algorithm
TAAI '10 Proceedings of the 2010 International Conference on Technologies and Applications of Artificial Intelligence
An efficient algorithm for solving nonograms
Applied Intelligence
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
Teaching Advanced Features of Evolutionary Algorithms Using Japanese Puzzles
IEEE Transactions on Education
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
An evolutionary-based hyper-heuristic approach for the Jawbreaker puzzle
Applied Intelligence
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A Taguchi-based genetic algorithm (TBGA) is proposed to solve Japanese nonogram puzzles. The TBGA exploits the power of global exploration inherent in the traditional genetic algorithm (GA) and the abilities of the Taguchi method in efficiently generating offspring. In past researches, the GA with binary encoding and inappropriate fitness functions makes a huge search space size and inaccurate direction for searching the solution of a nonogram. Consequently, the GA does not easily converge to the solution. The proposed TBGA includes the effective condensed encoding, the improved fitness function, the modified crossover, the modified mutation, and the Taguchi method for solving Japanese nonograms. The systematic reasoning ability of the Taguchi method is incorporated in the modified crossover operation to select the better genes to achieve crossover, and eventually enhance the GA. In this study, the condensed encoding can make sure that the chromosome is a feasible solution in all rows for Japanese nonograms. In the reconstruction process of a Japanese nonogram, the numbers in the left column are used as encoding conditions, and the numbers in the top row with the improved fitness function are employed to evaluate the reconstruction result. From the computational experiments, the proposed TBGA approach is effectively applied to solve nonograms and better than a GA does.