Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often show ...surprisingly good performance on widely used DTLZ and WFG test problems. The performance of those algorithms has continued to be improved. The aim of this paper is to show our concern that such a performance improvement race may lead to the overspecialization of developed algorithms for the frequently used many-objective test problems. In this paper, we first explain the DTLZ and WFG test problems. Next, we explain many-objective evolutionary algorithms characterized by the use of systematically generated weight vectors. Then we discuss the relation between the features of the test problems and the search mechanisms of weight vector-based algorithms such as multiobjective evolutionary algorithm based on decomposition (MOEA/D), nondominated sorting genetic algorithm III (NSGA-III), MOEA/dominance and decomposition (MOEA/DD), and θ-dominance based evolutionary algorithm (θ-DEA). Through computational experiments, we demonstrate that a slight change in the problem formulations of DTLZ and WFG deteriorates the performance of those algorithms. After explaining the reason for the performance deterioration, we discuss the necessity of more general test problems and more flexible algorithms.
The performance of decomposition-based multiobjective evolutionary algorithms (MOEAs) often deteriorates clearly when solving multiobjective optimization problems with irregular Pareto fronts (PFs). ...The main reason is the improper settings of reference vectors and scalarizing functions. In this paper, we propose a decomposition-based MOEA guided by a growing neural gas network, which learns the topological structure of the PF. Both reference vectors and scalarizing functions are adapted based on the topological structure to enhance the evolutionary algorithm's search ability. The proposed algorithm is compared with eight state-of-the-art optimizers on 34 test problems. The experimental results demonstrate that the proposed method is competitive in handling irregular PFs.
We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto dominance-based, scalarizing function-based, ...and hypervolume-based algorithms. NSGA-II, MOEA/D, SMS-EMOA, and HypE are examined using knapsack problems with 2-10 objectives. Our test problems are generated by randomly specifying coefficients (i.e., profits) in objectives. We also generate other test problems by combining two objectives to create a dependent or correlated objective. Experimental results on randomly generated many-objective knapsack problems are consistent with well-known performance deterioration of Pareto dominance-based algorithms. That is, NSGA-II is outperformed by the other algorithms. However, it is also shown that NSGA-II outperforms the other algorithms when objectives are highly correlated. MOEA/D shows totally different search behavior depending on the choice of a scalarizing function and its parameter value. Some MOEA/D variants work very well only on two-objective problems while others work well on many-objective problems with 4-10 objectives. We also obtain other interesting observations such as the performance improvement by similar parent recombination and the necessity of diversity improvement for many-objective knapsack problems.
In general, an M-objective continuous optimization problem has an (M - 1)-dimensional Pareto front in the objective space. If its dimension is smaller than (M - 1), it is called a degenerate Pareto ...front. Deb-Thiele-Laumanns-Zitzler (DTLZ)5 and Walking Fish Group (WFG)3 have often been used as many-objective continuous test problems with degenerate Pareto fronts. However, it was noted that DTLZ5 has a nondegenerate part of the Pareto front. Constraints have been proposed to remove the nondegenerate part. In this letter, first we show that WFG3 also has a nondegenerate part. Then, we derive constraints to remove the nondegenerate part. Finally, we show that the existence of the nondegenerate part makes WFG3 an interesting test problem through computational experiments.
This paper investigates the problem of the stock closing price forecasting for the stock market. Based on existing two-stage fusion models in the literature, two new prediction models based on ...clustering have been proposed, where k-means clustering method is adopted to cluster several common technical indicators. In addition, ensemble learning has also been applied to improve the prediction accuracy. Finally, a hybrid prediction model, which combines both the k-means clustering and ensemble learning, has been proposed. The experimental results on a number of Chinese stocks demonstrate that the hybrid prediction model obtains the best predicting accuracy of the stock price. The k-means clustering on the stock technical indicators can further enhance the prediction accuracy of the ensemble learning.
The frequently used basic version of MOEA/D (multi-objective evolutionary algorithm based on decomposition) has no normalization mechanism of the objective space, whereas the normalization was ...discussed in the original MOEA/D paper. As a result, MOEA/D shows difficulties in finding a set of uniformly distributed solutions over the entire Pareto front when each objective has a totally different range of objective values. Recent variants of MOEA/D have normalization mechanisms for handling such a scaling issue. In this paper, we examine the effect of the normalization of the objective space on the performance of MOEA/D through computational experiments. A simple normalization mechanism is used to examine the performance of MOEA/D with and without normalization. These two types of MOEA/D are also compared with recently proposed many-objective algorithms: NSGA-III, MOEA/DD, and
θ
-DEA. In addition to the frequently used many-objective test problems DTLZ and WFG, we use their minus versions. We also propose two variants of the DTLZ test problems for examining the effect of the normalization in MOEA/D. Test problems in one variant have objective functions with totally different ranges. The other variant has a kind of deceptive nature, where the range of each objective is the same on the Pareto front but totally different over the entire feasible region. Computational experiments on those test problems clearly show the necessity of the normalization. It is also shown that the normalization has both positive and negative effects on the performance of MOEA/D. These observations suggest that the influence of the normalization is strongly problem dependent.
Evolutionary many-objective optimization: A short review Ishibuchi, H.; Tsukamoto, N.; Nojima, Y.
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence),
06/2008
Conference Proceeding
Recenzirano
Whereas evolutionary multiobjective optimization (EMO) algorithms have successfully been used in a wide range of real-world application tasks, difficulties in their scalability to many-objective ...problems have also been reported. In this paper, first we demonstrate those difficulties through computational experiments. Then we review some approaches proposed in the literature for the scalability improvement of EMO algorithms. Finally we suggest future research directions in evolutionary many-objective optimization.
Adhesion GPCRs (aGPCRs) are a subfamily of GPCRs that are involved in cell adhesion, cell proliferation, and cell migration in various tissues. G protein‐coupled receptor proteolytic site (GPS) of ...aGPCR is required to cleave the extracellular domain autocatalytically, generating two fragments; a N‐terminal fragment (NTF) and a C‐terminal fragment (CTF) containing seven transmembrane structure. NTF can interact with CTF non‐covalently after cleavage, however the physiological significance of the cleavage of aGPCR at GPS, and also the interaction between NTF and CTF have not been fully clarified yet. In this study, we first investigated the expression profiles of two aGPCRs, GPR56/ADGRG1, and LPHN1/ADGRL1 in mouse brain, and found that the NTF and CTF of GPR56 independently expressed in different brain region at different developmental stages. Immunoprecipitation of GPR56CTF co‐immunoprecipitated LPHN1NTF from mouse brain and HEK293T cells expressing both fragments. Stimulation with LPHN1 ligand, α‐Latrotoxin N4C (αLTXN4C), to cells expressing LPHN1NTF and GPR56CTF increased intracellular Ca2+ concentration (Ca2+i). We also demonstrated that GPR56KO mouse neurons attenuated their Ca2+ response to αLTXN4C. These results suggest the possibility of functional and chimeric complex containing LPHN1NTF and GPR56CTF in neuronal signal transduction.
This paper indicates the possibility of functional and chimeric complex derived from two different adhesion GPCRs, GPR56, and Latrophilin1, in neuron. The complex is formed by N‐terminal fragment and C‐terminal fragment of different adhesion GPCRs and mediates the signal received by extracellular N‐terminal fragment of Latrophilin1. The results provide a new model of membrane receptor complex.
The main advantage of multi-objective genetic fuzzy systems (MoGFS) is that a number of non-dominated fuzzy rule-based systems are obtained along the tradeoff surface among conflicting objectives. ...Accuracy maximization, complexity minimization and interpretability maximization have often been used for multi-objective design of fuzzy rule-based classifiers. A number of non-dominated fuzzy rule-based classifiers are obtained by a single run of MoGFS. A human decision maker is supposed to choose a single final classifier from a number of obtained classifiers according to his/her preference. One problem, which has not been discussed in many studies on MoGFS, is how to choose a single final classifier. In this paper, we discuss classifier selection with no intervention of the decision maker. Whereas complexity and interpretability are very important factors in classifier selection, we concentrate on the maximization of generalization ability as the first step towards a more general handling of classifier selection. We propose the use of repeated double cross-validation (rdCV) to choose a single final classifier and to evaluate the generalization ability of the selected classifier. We also discuss how our approach can be applied to parameter specification, formulation selection and algorithm choice.
This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ...ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.
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