ITSO: a novel inverse transform sampling-based optimization algorithm for stochastic search

Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods Engineering Business applications. Following recent works on AI’s theoretical deficiencies, a rigour context for optimization problem black-box objective function is developed. The algorithm stems directly from theory probability, instead presumed inspiration. Thus convergence properties proposed methodology are inherently stable. In particular, optimizer utilizes an algorithmic implementation n-dimensional inverse transform sampling as search strategy. No control parameters required to be tuned, trade-off among exploration exploitation is, by definition, satisfied. A proof provided, concluding that when falling into framework, either or incidentally, any converges. numerical experiments verify results efficacy apropos reaching sought optimum.