Abstract:Global optimization problems are ubiquitous to engineering and the sciences. Many such problems are not amenable to analytical techniques and examinations of some potential solutions for these problems often suggest that hill-climbing algorithms would be unable to navigate the jagged and confusing terrain. Despite this, genetic programming is often applied to these problems in the hopes that it will be able to identify high-quality solutions. We suspect that genetic programming would perform no better than ran… Show more
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