用于生理信号情感识别的自适应遗传算法

针对用生理信号识别情感中的最优情感特征组合的选择这一组合优化问题,将遗传算法中的交叉、变异操作加以改进形成新的算法.该算法用来选择最能代表相应情感状态的最优特征组合,并以最近邻法的分类正确率作为当前搜索到的最优特征组合的评价准则,对joy、anger、pleasure、sadness这4种情感状态进行识别,得到了较好的情感识别效果.仿真实验表明了该方法的可行性和有效性.     

Evolving evolutionary algorithms using linear genetic programming

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.     

Evolving strategy for a probabilistic game of imperfect information using genetic programming

We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitnessless selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt’s emergence, assess its direct and indirect human-competitiveness, and describe the behavioral patterns observed in its strategy.

---

Evolving choice structures for genetic programming

It is quite difficult but essential for Genetic Programming (GP) to evolve the choice structures. Traditional approaches usually ignore this issue. They define some “if-structures” functions according to their problems by combining “if-else” statement, conditional criterions and elemental functions together. Obviously, these if-structure functions depend on the specific problems and thus have much low reusability. Based on this limitation of GP, in this paper we propose a kind of termination criterion in the GP process named “Combination Termination Criterion” (CTC). By testing CTC, the choice structures composed of some basic functions independent to the problems can be evolved successfully. Theoretical analysis and experiment results show that our method can evolve the programs with choice structures effectively within an acceptable additional time.     

Ontology-based context modeling for emotion recognition in an intelligent web

We describe an ontological model for representation and integration of electroencephalographic (EEG) data and apply it to detect human emotional states. The model (BIO_EMOTION) is an ontology-based context model for emotion recognition and acts as a basis for: (1) the modeling of users’ contexts, including user profiles, EEG data, the situation and environment factors, and (2) supporting reasoning on the users’ emotional states. Because certain ontological concepts in the EEG domain are ill-defined, we formally represent and store these concepts, their taxonomies and high-level representation (i.e., rules) in the model. To evaluate the effectiveness for inferring emotional states, DEAP dataset is used for model reasoning. Result shows that our model reaches an average recognition ratio of 75.19 % on Valence and 81.74 % on Arousal for eight participants. As mentioned above, the BIO-EMOTION model acts like a bridge between users’ emotional states and low-level bio-signal features. It can be integrated in user modeling techniques, and be used to model web users’ emotional states in human-centric web aiming to provide active, transparent, safe and reliable services to users. This work aims at, in other words, creating an ontology-based context model for emotion recognition using EEG. Particularly, this model completely implements the loop body of the W2T data cycle once: from low-level EEG feature acquisition to emotion recognition. A long-term goal for the study is to complete this model to implement the whole W2T data cycle.     

Parsimonious genetic programming for complex process intelligent modeling: algorithm and applications

A novel genetic programming (GP) algorithm called parsimonious genetic programming (PGP) for complex process intelligent modeling was proposed. First, the method uses traditional GP to generate nonlinear input–output model sets that are represented in a binary tree structure according to special decomposition method. Then, it applies orthogonal least squares algorithm (OLS) to estimate the contribution of the branches, which refers to basic function term that cannot be decomposed anymore, to the accuracy of the model, so as to eliminate complex redundant subtrees and enhance convergence speed. Finally, it obtains simple, reliable and exact linear in parameters nonlinear model via GP evolution. Simulations validate that the proposed method can generate more robust and interpretable models, which is obvious and easy for realization in real applications. For the proposed algorithm, the whole modeling process is fully automatic, which is a rather promising method for complex process intelligent modeling.     

Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems

We solve the multi-objective flexible job-shop problems by using dispatching rules discovered through genetic programming. While Simple Priority Rules have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite dispatching rules have been shown to be more effective as they are constructed through human experience. In this paper, we evaluate and employ suitable parameter and operator spaces for evolving composite dispatching rules using genetic programming, with an aim towards greater scalability and flexibility. Experimental results show that composite dispatching rules generated by our genetic programming framework outperforms the single dispatching rules and composite dispatching rules selected from literature over five large validation sets with respect to minimum makespan, mean tardiness, and mean flow time objectives. Further results on sensitivity to changes (in coefficient values and terminals among the evolved rules) indicate that their designs are robust.     

Evolving complex group behaviors using genetic programming with fitness switching

Genetic programming provides a useful tool for emergent computation and artificial life research. However, conventional genetic programming is not efficient enough to solve realistic multiagent tasks consisting of several emergent behaviors that need to be coordinated in the proper sequence. In this paper, we describe a novel method, called fitness switching, for evolving composite cooperative behaviors in multiple robotic agents using genetic programming. The method maintains a pool of basis fitness functions which are switched from simpler ones to more complex ones. The performance is demonstrated and evaluated in the context of a table transport problem. Experimental results show that the fitness switching method is an effective mechanism for evolving collective behaviors which can not be solved by simple genetic programming.     

Evolving recursive programs by using adaptive grammar based genetic programming

The publisher apologizes for an error that occurred in the above mentioned article. The error appears in the printed version, as well as in the html and pdf version online. Man Leung Wong is the sole author of this article. His affiliation is listed below. The online version of the original article can be found at     

Morphological algorithm design for binary images using genetic programming

This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...     

Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm

Multimedia Tools and Applications - Facial Emotion Recognition (FER) is an important research domain which allows us to provide a better interactive environment between humans and computers. Some...     

Fuzzy multilevel programming with a hybrid intelligent algorithm

In order to model fuzzy decentralized decision-making problem, fuzzy expected value multilevel programming and chance-constrained multilevel programming are introduced. Furthermore, fuzzy simulation, neural network, and genetic algorithm are integrated to produce a hybrid intelligent algorithm for finding the Stackelberg-Nash equilibrium. Finally, two numerical examples are provided to illustrate the effectiveness of the hybrid intelligent algorithm.     

An approach of genetic programming for music emotion classification

In this paper, we suggest a new approach of genetic programming for music emotion classification. Our approach is based on Thayer’s arousal-valence plane which is one of representative human emotion models. Thayer’s plane which says human emotions is determined by the psychological arousal and valence. We map music pieces onto the arousal-valence plane, and classify the music emotion in that space. We extract 85 acoustic features from music signals, rank those by the information gain and choose the top k best features in the feature selection process. In order to map music pieces in the feature space onto the arousal-valence space, we apply genetic programming. The genetic programming is designed for finding an optimal formula which maps given music pieces to the arousal-valence space so that music emotions are effectively classified. k-NN and SVM methods which are widely used in classification are used for the classification of music emotions in the arousal-valence space. For verifying our method, we compare with other six existing methods on the same music data set. With this experiment, we confirm the proposed method is superior to others.     

Evolving reliable and robust controllers for real robots by genetic programming

 Using Genetic Programming (GP)-based approaches to evolve robot controllers has the advantage of operating variable-size genotype. This is an important feature for evolving robot control systems as it allows complete freedom for the control architecture in respect to the task complexity which is difficult to predict. However, GP-based work in evolving controllers has been questioned in the verification of the performance on real robots, the generalisation of defining primitives, and the computational cost needed. In this paper, we present our GP framework in which a special representation of the robot controller is designed; this representation can capture well the characteristic of a behaviour controller so that our system can efficiently evolve desired robot behaviours by a relatively low computational cost. This system has been successfully used to evolve reliable and robust controllers working on a real robot, for a variety of tasks.     

Process industry scheduling optimization using genetic algorithm and mathematical programming

This article addresses the problem of scheduling in oil refineries. The problem consists of a multi-product plant scheduling, with two serial machine stages—a mixer and a set of tanks—which have resource constraints and operate on a continuous flow basis. Two models were developed: the first using mixed-integer linear programming (MILP) and the second using genetic algorithms (GA). Their main objective was to meet the whole forecast demand, observing the operating constraints of the refinery and minimizing the number of operational changes. A real-life data-set related to the production of fuel oil and asphalt in a large refinery was used. The MILP and GA models proved to be good solutions for both primary objectives, but the GA model resulted in a smaller number of operational changes. The reason for this is that GA incorporates a multi-criteria approach, which is capable of adaptively updating the weights of the objective throughout the evolutionary process.     

Method for solving nonlinear goal programming with interval coefficients using genetic algorithm

Traditional formulations on reliability optimization problems have assumed that the coefficients of models are known as fixed quantities and reliability design problem is treated as deterministic optimization problems. Because that the optimal design of system reliability is resolved in the same stage of overall system design, model coefficients are highly uncertainty and imprecision during design phase and it is usually very difficult to determine the precise values for them. However, these coefficients can be roughly given as the intervals of confidence.

In this paper, we formulated reliability optimization problem as nonlinear goal programming with interval coefficients and develop a genetic algorithm to solve it. The key point is how to evaluate each solution with interval data. We give a new definition on deviation variables which take interval relation into account. Numerical example is given to demonstrate the efficiency of the proposed approach.     

智能Agent程序设计语言IAPL

分析了现有的面向Agent程序设计语言,指出了各自的优缺点。基于智能Agent结构,设计了一个新的智能Agent程序设计语言IAPL;给出了IAPL的语言规范、一组操作原语(信念、意向等心智成份的增加、删除、修改、查询等原语)及Agent之间的通信原语;利用情境演算理论,提供了IAPL语言的语义;提出了在线执行与离线规划相结合的IAPL程序执行方式。     

Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir

In this work we investigate how artificial neural network (ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neural network. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid genetic algorithm–neural network strategy (GA–ANN). The proposed algorithm combines the local searching ability of the gradient–based back-propagation (BP) strategy with the global searching ability of genetic algorithms. Genetic algorithms are used to decide the initial weights of the gradient decent methods so that all the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set avoiding premature convergence and permutation problems. For an evaluation purpose, the performance and generalization capabilities of GA–ANN are compared with those of models developed with the common technique of BP. The results demonstrate that carefully designed genetic algorithm-based neural network outperforms the gradient descent-based neural network.