Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Theory of fuzzy integrals and its applications

Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.

https://dl.acm.org/doi/pdf/10.1145/2792984

Many-Objective Evolutionary Algorithms: A Survey

Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two_Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two_Arch, we propose a significantly improved two-archive algorithm (i.e., Two_Arch2) for ManyOPs in this paper. In our Two_Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new ${L} _{\mathbf {p}}$ -norm-based ( ${p}~\boldsymbol { ) diversity maintenance scheme for ManyOPs in Two_Arch2. In order to evaluate the performance of Two_Arch2 on ManyOPs, we have compared it with several MOEAs on a wide range of benchmark problems with different numbers of objectives. The experimental results show that Two_Arch2 can cope with ManyOPs (up to 20 objectives) with satisfactory convergence, diversity, and complexity.

Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization

In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.

Mobile robot path planning using membrane evolutionary artificial potential field

Since multiobjective evolutionary algorithms (MOEAs) provide a set of nondominated solutions, decision making of selecting a preferred one out of them is required in real applications. However, there has been some research on MOEA in which the user's preferences are incorporated for this purpose. This paper proposes preference-based solution selection algorithm (PSSA) by which user can select a preferred one out of nondominated solutions obtained by any one of MOEAs. The PSSA, which is a kind of multiple criteria decision making (MCDM) algorithm, represents user's preference to multiple objectives or criteria as a degree of consideration by fuzzy measure and globally evaluates obtained solutions by fuzzy integral. The PSSA is also employed in each and every generation of evolutionary process to propose multiobjective quantum-inspired evolutionary algorithm with preference-based selection (MQEA-PS). To demonstrate the effectiveness of PSSA and MQEA-PS, computer simulations and real experiments on evolutionary multiobjective optimization for the fuzzy path planner of mobile robot are carried out. Computer simulation and experiment results show that the user's preference is properly reflected in the selected solution. Moreover, MQEA-PS shows improved performance for the DTLZ problems and fuzzy path planner optimization problem compared to MQEA with dominance-based selection and other MOEAs like NSGA-II and MOPBIL.

Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

This paper proposes a novel evolutionary multiobjective footstep planner for humanoid robots. First, a footstep planner using a univector field navigation method is proposed to provide a command state (CS), which is to be an input of a modifiable walking pattern generator (MWPG) at each footstep. Then, the MWPG generates corresponding trajectories for every leg joint of the humanoid robot at each footstep to follow the CS. Second, a multiobjective evolutionary algorithm (MOEA) is employed to optimize the univector fields satisfying multiple objectives in navigation. Finally, a preference-based selection algorithm based on a fuzzy measure and fuzzy integral is proposed to select the preferred one out of various nondominated solutions obtained by the MOEA. The effectiveness of the proposed evolutionary multiobjective footstep planner is demonstrated through computer simulations for a simulation model of a small-sized humanoid robot, HanSaRam-VIII.

Evolutionary Multiobjective Footstep Planning for Humanoid Robots

To make artificial creatures deliberately interact with their environment like living creatures, a behavior selection method mimicking living creatures thought mechanism is needed. For this purpose, there has been research based on probabilistic knowledge links between input (assumed fact) and target (behavior) symbols for reasoning. However, real intelligent creatures including human beings select a behavior based on the multi-criteria decision making process using the degree of consideration (DoC) for input symbols, i.e. will and context symbols, in their memory. In this paper, the DoC-based mechanism of thought (DoC-MoT) is proposed and applied to the behavior selection of artificial creatures. The knowledge links between input and behavior symbols are represented by the partial evaluation values of behaviors over each input symbol, and the degrees of consideration for input symbols are represented by the fuzzy measures. The proposed method selects a behavior through global evaluation by the fuzzy integral, as a multicriteria decision making process, of knowledge link strengths with respect to the fuzzy measure values. The effectiveness of the proposed behavior selection method is demonstrated by experiments carried out with a synthetic character Rity in the 3D virtual environment. The results show that the artificial creatures with various characteristics can be successfully created by the proposed DoC-MoT. Moreover, training the created artificial creatures to modify their characteristics was more efficient in the DoC-MoT than the probability-based mechanism of thought (P-MoT), both in terms of the number of parameters to be set and the amount of time consumed.

The Degree of Consideration-Based Mechanism of Thought and Its Application to Artificial Creatures for Behavior Selection

Considering the human-robot symbiosis in coming years, robots should be able to read human intention for natural and rational interaction with human beings. This paper proposes effective human-robot interaction (HRI) by reading the human intention using cognitive architecture. Proposed intention reading algorithm is inspired by mirror-neuron system and simulation theory which are the significant parts of human mind reading skill. For human intention reading, the cognitive architecture consisting of eight modules, i.e. perception, attention, behavior mapping, behavior model, intention reading, memory, behavior selection, and actuator modules is also proposed. The effectiveness of the proposed scheme is demonstrated through computer simulations on human-robot play with two different objects, such as a ball and a toy car.

Human-robot interaction by reading human intention based on mirror-neuron system

The wireless sensor network is a decentralized and self-organized system. Each sensor node in the sensor network should be intelligent enough to carry out its task of monitoring the environment. There would be numerous ways for deploying the sensor nodes in the environment. In this paper, swarm intelligence-based sensor network deployment strategy is proposed. To make a reference point for each sensor node, fuzzy integral is utilized as a multi-criteria decision making process. Three criteria, such as sensor value, crowdedness and confidence, are used for partial evaluation and the degree of consideration for each criterion is represented by fuzzy measure. Global evaluation by fuzzy integral determines the best position for each sensor node independently. To show the effectiveness of the proposed strategy, it is compared with the SPSO07-based deployment strategy through computer simulations in a simulation environment. The results show that the proposed strategy covers much wider area with sensor nodes than the SPSO07-based one.

Ji-Hyeong Han

Papers

Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

Evolutionary Multiobjective Footstep Planning for Humanoid Robots

The Degree of Consideration-Based Mechanism of Thought and Its Application to Artificial Creatures for Behavior Selection

Human-robot interaction by reading human intention based on mirror-neuron system

Swarm intelligence-based sensor network deployment strategy