Quantum-inspired Evolutionary Algorithm and Differential Evolution Used in the Adaptation of Segmentation Parameters
نویسنده
چکیده
The key step in object-based image interpretation is segmentation. Frequently the relationship between the segmentation parameter values and the corresponding segmentation outcome is not obvious, and the definition of suitable parameter values is usually a time consuming, trial and error process. In (Costa et al., 2008), a supervised, semi-automatic method for the adaptation of segmentation parameters was proposed. Initially a human operator delineates polygons enclosing a representative set of target image objects. The manually drawn polygons are then used as reference segments by a Genetic Algorithm (GA), which searches the segmentation parameter space for values that produce segments as similar as possible to the reference. Although GA based methods have been successfully applied in many optimization problems, they are characterized by a high computational load, and do not guarantee that optimal values are found. Alternatives to the basic GA model have been proposed in order to accelerate convergence, preventing at the same time convergence to local maxima. In this work two of such alternatives have been investigated: Quantum-Inspired Evolutionary Algorithm (QIEA) (Abs da Cruz, 2007) and Differential Evolution (DE) (Storn and Price, 1997). Those two models were employed in the method proposed in (Costa et al., 2008), substituting the conventional GA originally used. Experiments showed that both models converge significantly faster than the original GA. Additionally, for an equivalent computational load, dissimilarity among the reference segments and the ones generated with the parameter values found by applying QIEA and DE was in average respectively 44% and 50% lower, when compared to the results obtained with the original GA.
منابع مشابه
Fuzzy logic controlled differential evolution to solve economic load dispatch problems
In recent years, soft computing methods have generated a large research interest. The synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. A particular evolutionary algorithm (EA) is differential evolution (DE). As for any EA, DE algorithm also requires parameters tuning to achieve desirable performance. In this paper tuning the perturbation factor vector of DE ...
متن کاملFuzzy logic controlled differential evolution to solve economic load dispatch problems
In recent years, soft computing methods have generated a large research interest. The synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. A particular evolutionary algorithm (EA) is differential evolution (DE). As for any EA, DE algorithm also requires parameters tuning to achieve desirable performance. In this paper tuning the perturbation factor vector of DE ...
متن کاملQuantum-Inspired Differential Evolution with Particle Swarm Optimization for Knapsack Problem
This paper presents a new hybrid algorithm called QDEPSO (Quantum inspired Differential Evolution with Particle Swarm Optimization) which combines differential evolution (DE), particle swarm optimization method (PSO) and quantum-inspired evolutionary algorithm (QEA) in order to solve the 0-1 optimization problems. In the initialization phase, the QDEPSO uses the concepts of quantum computing as...
متن کاملDeveloping Adaptive Differential Evolution as a New Evolutionary Algorithm, Application in Optimization of Chemical Processes
متن کامل
A Novel Fuzzy-Genetic Differential Evolutionary Algorithm for Optimization of A Fuzzy Expert Systems Applied to Heart Disease Prediction
This study presents a novel intelligent Fuzzy Genetic Differential Evolutionary model for the optimization of a fuzzy expert system applied to heart disease prediction in order to reduce the risk of heart disease. To this end, a fuzzy expert system has been proposed for the prediction of heart disease. The proposed model can be used as a tool to assist physicians. In order to: (1) tune the para...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008