Practical Implications of New Results in Conservation of Optimizer Performance

Abstract. Three theoretical perspectives upon conservation of performance in function optimization are outlined. In terms of statistical information, performance is conserved when the distribution of functions is such that all domain subsets of a given size have identically distributed random values. In terms of algorithmic information, performance is conserved because almost all functions are algorithmically random. Also, an optimizer's algorithmic complexity bounds the information gained or lost in its processing of the test function. The practical consequences of these theoretical results are explored, with emphasis upon the results from algorithmic information theory, which are new.

• Type: Conference
• Year: 2000
• Where: PPSN
• Authors: Thomas M. English
• Keywords: Algorithmic Information, Distributed And Parallel Computing, Optimizer's Algorithmic Complexity, Performance, PPSN 2000