With the increasing complexity of engineering problems, the traditional, single-objective and deterministic optimization method can not meet people’s requirements. A multi-objective fuzzy optimization model of resource input is built for M chlor-alkali chemical eco-industrial park in this paper. First, the model is changed into the form that can be solved by genetic algorithm using fuzzy theory...

The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Multi-Attribute Tradespace Exploration (MATE) for Survivability is introduced as a general methodology for survivability analysis and demonstrated through an application to a satellite radar system. MATE for Survivability applies decision theory to the parametric modeling of th...

Higher order mutation testing is considered a promising solution for overcoming the main limitations of first order mutation testing. Strongly subsuming higher order mutants (SSHOMs) are the most valuable among all kinds of higher order mutants (HOMs) generated by combining first order mutants (FOMs). They can be used to replace all of its constituent FOMs without scarifying test effectiveness....

A simple model of multi-agent three-dimensional construction is presented. The properties of this model are investigated. Based on these properties, a fitness function is defined to characterize the structured patterns that can be generated by the model. The fitness function assigns a value to each pattern. The choice of the fitness function is validated by the fact that human observers tend to...

We propose three operators based on MFF. The first uses MFF. The second uses MFF when applies Maximin-Constraint and uses modified MFF when applies Maximin-Clustering. The third uses modified MFF. According to the results, the three operators are competitive to solve multi-objective optimization problems having both low dimensionality (two or three) and high dimensionality (more than three) in ...

In this paper, we present a new type of multi-class learning algorithm called a linear-max algorithm. Linear-max algorithms learn with a special type of attribute called a sub-expert. A sub-expert is a vector attribute that has a value for each output class. The goal of the multi-class algorithm is to learn a linear function combining the sub-experts and to use this linear function to make corr...

Some domains, like robot soccer, are difficult for agents to learn in using direct statistical and reinforcement learning techniques. However, agents often have a goal or purpose, which gives them a natural basis for reinforcement learning in the form of a fitness function. Such a fitness function allows the task of learning to be accomplished through optimization of the fitness function in the...

With the continuous development of social software and multimedia technology, images have become a kind important carrier for spreading information socializing. How to evaluate an image comprehensively has focus recent researches. The traditional aesthetic assessment methods often adopt single numerical overall scores, which certain subjectivity can no longer meet higher requirements. In this a...