• Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach 

  Boutaib, S.; Elarbi, M.; Bechikh, S.; Coello, C.A.; Said, L. B. (2022-11-01)

  Context: Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap: Existing works did not take into account the issue of ...

• On the Construction of Pareto-Compliant Combined Indicators 

  Falcón-Cardona, J.G.; Emmerich, M.; Coello, C.A. (2022-08-12)

  The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect ...

• A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization 

  Li, L.; Li, Y.; Lin, Q.; Ming, Z.; Coello, C.A. (2022-10-01)

  Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection ...

• An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization 

  Lin, Q.; Wu, X.; Ma, L.; Li, J.; Gong, M.; Coello, C.A. (2022-08-01)

  Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, ...

• Multiple source transfer learning for dynamic multiobjective optimization 

  Ye, Y.; Lin, Q.; Ma, L.; Wong, K. C.; Gong, M.; Coello, C.A. (2022-08-01)

  Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs ...

• A self-organizing weighted optimization based framework for large-scale multi-objective optimization 

  Li, Y.; Li, L.; Lin, Q.; Wong, K. C.; Ming, Z.; Coello, C.A. (2022-07-01)

  The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization ...

• Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters 

  Fernández, E.; Rangel-Valdez, N.; Cruz-Reyes, L.; Gomez-Santillan, C.; Coello, C.A. (2022-07-01)

  Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP ...

• Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations 

  Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2022-06-01)

  The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization (MMO) methods currently available. However, some of its components ...

• A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering 

  Yu, Q.; Lin, Q.; Zhu, Z.; Wong, K. C.; Coello, C.A. (2022-06-01)

  In this paper, a dynamic multi-objective evolutionary algorithm is proposed based on polynomial regression and adaptive clustering, called DMOEA-PRAC. As the Pareto-optimal solutions and fronts of dynamic multi-objective ...

• AdaSwarm: Augmenting Gradient-Based Optimizers in Deep Learning with Swarm Intelligence 

  Mohapatra, R.; Saha, S.; Coello, C.A.; Bhattacharya, A.; Dhavala, S. S.; Saha, S. (2022-04-01)

  This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially ...

• Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking 

  Rivera, G.; Coello, C.A.; Cruz-Reyes, L.; Fernandez, E.R.; Gomez-Santillan, C.; Rangel-Valdez, N. (2022-03-01)

  In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multi-objective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the ...

• A General Framework Based on Walsh Decomposition for Combinatorial Optimization Problems 

  Unanue, I.; Merino, M.; Lozano, J.A. (2021-01-01)

  In this paper we pursue the use of the Fourier transform for a general analysis of combinatorial optimization problems. While combinatorial optimization problems are defined by means of different notions like weights in a ...

• A mathematical analysis of EDAs with distance-based exponential models 

  Unanue, I.; Merino, M.; Lozano, J.A. (2022-09-01)

  Estimation of Distribution Algorithms have been successfully used to solve permutation-based Combinatorial Optimization Problems. In this case, the algorithms use probabilistic models specifically designed for codifying ...

• PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods 

  Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2022-01-01)

  PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as ...

• Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking 

  Rivera, G.; Coello, C.A.; Cruz-Reyes, L.; Fernandez, E.R.; Gomez-Santillan, C.; Rangel-Valdez, N. (2022-03-01)

  In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multi-objective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the ...

• COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems 

  Llano García, J.L.; Monroy, R.; Sosa Hernández, V.A.; Coello, C.A. (2021-12-01)

  Many real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision ...

• Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey 

  Falcón-Cardona, J.G.; Hernández Gómez, R.; Coello, C.A.; Castillo Tapia, M.G. (2021-12-01)

  Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of ...

• A Novel Parametric benchmark generator for dynamic multimodal optimization 

  Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2021-08-01)

  In most existing studies on dynamic multimodal optimization (DMMO), numerical simulations have been performed using the Moving Peaks Benchmark (MPB), which is a two-decade-old test suite that cannot simulate some critical ...

• On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms 

  Falcon-Cardona, J.G; Ishibuchi, H.; Coello, C.A.; Emmerich, M. (2021-08-01)

  For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences ...

• CURIE: a cellular automaton for concept drift detection 

  Lobo, J.L.; Del Ser, J.; Osaba, E.; Bifet, A.; Herrera, F. (2021-11-01)

  Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as ...

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