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In this paper, a genetic algorithm for solving Sudoku puzzles is presented. Objective function has been defined as maximization of an entropy function in order to get a solution of Sudoku by generating rows, columns and 3x3 sub-matrices containing each ...
Automated architecture search has demonstrated significant success for image data, where reinforcement learning and evolution approaches now outperform the best human designed networks ([12], [8]). These successes have not transferred over to models ...
Syllables play an important role in speech synthesis, speech recognition, and spoken document retrieval. A novel, low cost, and language agnostic approach to dividing words into their corresponding syllables is presented. A hybrid genetic algorithm ...
The questions about nature that we can address using digital evolution are constrained by the speed of our software and the level of abstraction used in our genetic representations. One subject that has been particularly challenging is the evolution of ...
In this paper we present the recent accomplishments in developing a biclustering method based on evolutionary computation. In one of the recent papers we demonstrated the supremacy of our method over several state-of-the-art algorithms. We highlight the ...
Symbolic regression is used to estimate daily time series of local station precipitation amounts from global climate model output with a coarse spatial resolution. Local precipitation is of high importance in climate impact studies. Standard regression, ...
In genetic algorithms, the importance of the basis for representation has been well known. In this paper, we studied the effect of a good basis in binary representation, and resultantly we could show that a good basis improves the performance of search ...
It is usual to need an approximate model in evolutionary computation when fitness function is deemed to be abstract or expected to have a long computation time. In these cases, research on possibility of fitness approximation should proceed before ...
We introduce a method for optimizing parameters in convolution neural network (CNN) using a genetic algorithm (GA). In the experiment, 11 CNN parameters were chosen and considered as one chromosome. We generated 150 datasets were created by arbitrarily ...
This paper proposes a surrogate-assisted evolutionary algorithm for solving optimization problems with high calculation cost for constraint determination. The proposed method consists of CMOEA/D that extends the ability of MOEA/D to deal with ...
Tuning various parameters coexisting in genetic algorithms (GAs) has a direct impact on the performance of GA. Because of this, finding a proper parameter value is challenging. In this study, we use support vector regression to show the appropriate ...
The Tagged Visual Cryptography Scheme (TVCS)1 adds tag images to the noise-like shares generated by the traditional VCS to improve the shares management of the traditional VCS. However, the existing TVCSs suffers visual quality of the recovered secret ...
NeuroEvolution (NE) is a powerful method that uses Evolutionary Algorithms (EAs) for learning Artificial Neural Networks (ANNs). However, NE's performance is determined by the definition of dozens of parameters that guide the search of the EAs. In this ...
Deep learning is a widely explored research area, as it established the state of the art in many fields. However, the effectiveness of deep neural networks (DNNs) is affected by several factors related with their training. The commonly used gradient-...
In this work, we describe a self-replication-based mechanism for designing agents of increasing complexity. We demonstrate the validity of this approach by solving simple, standard evolutionary computation problems in simulation. In the context of these ...
Nvidia's CUDA parallel computation is a good way to reduce computational cost when applying a filter expressed by an equation to an image. In fact, programs need to be compiled to build GPU kernels. Over the past decade, various implementation methods ...
Minimal trees in polygonal maps aim at minimizing the connectivity in a network while avoiding obstacle collision. Being closely related to the Steiner Tree Problem, yet with a different scope, minimal trees aim at connecting origin-destination pairs, ...
Database intrusion detection (DB-IDS) is the problem of detecting anomalous queries in transaction systems like e-commerce platform. The adaptive detection algorithm is necessary to find anomaly accesses when the environment changes continuously. To ...
Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the ...
Learning surrogates for product design and optimization is potential to capitalize on competitive market segments. In this paper we propose an approach to learn surrogates of product performance from historical clusters by using ensembles of Genetic ...