This study presents the analysis and principle of an innovative optimizer named weIghted
meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean …

Here, we present a preselected small set of ordered structures (PSSOS) method, a first
principles-based high fidelity (HF), high throughput (HT) approach, for fast screening of the …

Finding the best configuration of algorithms' hyperparameters for a given optimization
problem is an important task in evolutionary computation. We compare in this work the …

We examine the zero-temperature Metropolis Monte Carlo (MC) algorithm as a tool for
training a neural network by minimizing a loss function. We find that, as expected on …

This work presents a detailed, comprehensive study of a new structural design of protective
devices with a circular cross-section (CS)(protective cables) for protecting equipment …

Extreme learning machine (ELM) is a revolutionary approach for training single-hidden layer
feedforward neural networks that combines both high performance and rapid learning …

Structurally and functionally isolated domains in biological macromolecular evolution, both
natural and artificial, are largely similar to “schemata”, building blocks (BBs), in evolutionary …

Evolutionary computing (EC) is an area of computer sciences and applied mathematics
covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively …

Nature-inspired optimization algorithms are defined as a class of algorithms that are inspired
by natural phenomena, eg, evolutionary algorithms and simulated annealing. These …

This paper analyzes bitstring functions, character-izes genes according to their contribution
to the genome's fitness, and proposes an optimization algorithm (G ABO) that uses this …