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In this paper, a hybrid quantum-inspired evolutionary algorithm (QIEA) is proposed to automatically design regularised ensemble extreme learning machines (EELMs). Quantum evolutionary computing is a relatively recent spot-lighted concept which takes advantage from both the evolutionary and quantum computing laws. In general, QIEAs have been proven to be really powerful for optimising complex engineering tasks. The fascinating trait of observation operator in QIEA enables us to transform the quantum bits to both the binary and continuous spaces. Here, the authors present a mix continuous/binary version of QIEA, to find out whether it is suited for designing regularised EELMs. Indeed, the design process of EELM is conducted at two different levels, i.e. hyper and low levels. At the low level, some novel criteria are presented in the form of penalty functions to enable the optimiser searching for parsimonious, compact and accurate regularised extreme learning machines, as individual components of the ensemble. At the hyper-level, the non-negative least square error optimisation technique is utilised to deterministically find the most eligible components for designing the ensemble. Through extensive numerical experiments, the authors demonstrate that the proposed method is really efficient for the automated design of EELM identifiers. [ABSTRACT FROM AUTHOR]

In this study, a comprehensive empirical test is conducted to analyse the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover, Laplace crossover and simulated binary crossover operators for the global optimization of continuous problems. To do so, 13 well-known numerical benchmark problems in three distinctive dimensions, namely 50D, 100D and 200D, are considered and the genetic algorithm (GA) with simple version and chaos-enhanced versions of the mentioned crossover operators are utilized for optimizing these functions. Furthermore, a time complexity analysis is conducted to find out the impact of hybridizing the chaos and the evolutionary operators on the computational complexity of GA. The results of the experimental analysis provide us with fruitful information regarding the scalability, computational complexity and exploration/exploitation capability of the considered rival optimization algorithms, as well as, demonstrate the efficacy of chaos-evolutionary computing for numerical continuous optimizations. [ABSTRACT FROM AUTHOR]

In this investigation, the authors intend to find out whether chaos-embedded heuristic walks with nature-based controllers are capable of increasing the performance of the standard random walks of evolutionary algorithms. Indeed, the research motivation emanates in the pursuit of addressing an increasing interest in using chaos sequences for modifying the exploitation capability of evolutionary optimisers. Here, the authors propose a nature-based controller to dynamically balance the exploration/exploitation capability of the evolutionary optimiser. To test the validity of the proposed method, a number of well-known Gaussian distribution-based evolutionary operators are considered, and their random parameters are changed with discrete-time chaotic sequences. Besides, to obtain authentic and reliable results regarding the accuracy and robustness of the rival techniques, the authors went through the existing literature and extracted 23 scalable, multimodal, constraint and unconstraint numerical benchmark problems and one engineering problem, i.e. optimal control of shape memory alloy actuators. The convergence rate and computational time of the resulting self-controlled chaos-based evolutionary approaches are analysed to find out whether they are capable of stabilising the chromosomes within a logical time. The results of the numerical experiments indicate that the self-controlled chaos-enhanced heuristic walks significantly increase the global searching capability of the evolutionary optimisers and have an aptitude to show a robust performance. [ABSTRACT FROM PUBLISHER]