On the pathological behavior of adaptive differential evolution on hybrid objective functions

Most state-of-the-art Differential Evolution (DE) algorithms are adaptive DEs with online parameter adaptation. We investigate the behavior of adaptive DE on a class of hy-brid functions, where independent groups of variables are associated with different component objective functions. An experimental evaluation of 3 state-of-the-art adaptive DEs (JADE, SHADE, jDE) shows that hybrid functions are "ada-ptive-DE-hard". That is, adaptive DEs have signicant fail-ure rates on these new functions. In-depth analysis of the adaptive behavior of the DEs reveals that their parameter adaptation mechanisms behave in a pathological manner on this class of problems, resulting in over-adaptation for one of the components of the hybrids and poor overall performance. Thus, this class of deceptive benchmarks pose a signicant challenge for DE

A novel fuzzy and multi-objective evolutionary algorithm based gene assignment for clustering short time series expression data

Conventional clustering algorithms based on Euclidean distance or Pearson correlation coefficient are not able to include order information in the distance metric and also unable to distinguish between random and real biological patterns. We present template based clustering algorithm for time series gene expression data. Template profiles are defined based on up-down regulation of genes between consecutive time points. Assignment of genes to templates is based on fuzzy membership function. Multi-objective evolutionary algorithm is used to determine compact clusters with varying number of templates. Statistical significance of each template is determined using permutation based non-parametric test. Statistically significant profiles are further tested for their biological relevance using gene ontology analysis. The algorithm was able to distinguish between real and noisy pattern when tested on artificial and real biological data. The proposed algorithm has shown better or similar performance compared to STEM and better than k-means on a real biological data

Multi-class protein fold recognition using multi-objective evolutionary algorithms

Protein fold recognition (PFR) is an important approach to structure discovery without relying on sequence similarity. In pattern recognition terminology, PFR is a multiclass classification problem to be solved by employing feature analysis and pattern classification techniques. This work reformulates PFR into a multiobjective optimization problem and proposes a multiobjective feature analysis and selection algorithm (MOFASA). We use support vector machines as the classifier. Experimental results on the structural classification of protein (SCOP) data set indicate that MOFASA is capable of achieving comparable performances to the existing results. In addition, MOFASA identifies relevant features for further biological analysis

Benchmark generator for CEC 2009 competition on dynamic optimization

Evolutionary algorithms(EAs) have been widely applied to solve stationary optimization problems. However, many real-world applications are actually dynamic. In order to study the performance of EAs in dynamic environments, one important task is to develop proper dynamic benchmark problems. Over the years, researchers have applied a number of dynamic test problems to compare the performance of EAs in dynamic environments, e.g., the “moving peaks ” benchmark (MPB) proposed by Branke [1], the DF1 generator introduced by Morrison and De Jong [6], the singleand multi-objective dynamic test problem generator by dynamically combining different objective functions of exiting stationary multi-objective benchmark problems suggested by Jin and Sendhoff [2], Yang and Yao’s exclusive-or (XOR) operator [10, 11, 12], Kang’s dynamic traveling salesman problem (DTSP) [3] and dynamic multi knapsack problem (DKP), etc. Though a number of DOP generators exist in the literature, there is no unified approach of constructing dynamic problems across the binary space, real space and combinatorial space so far. This report uses the generalized dynamic benchmark generator (GDBG) proposed in [4], which construct dynamic environments for all the three solution spaces. Especially, in the rea

A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments.Comment: 16 page