Advances in genetic programming
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
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Everybody makes mistakes -- we all make one eventually if we just work hard enough! This is good news and bad news. We learn from mistakes but mistakes are also painful and could turn out to be costly in terms of money, reputation and credibility. One is prone to make mistakes particularly with new and complex techniques with unknown or not exactly known properties. This paper talks about some of my more unfortunate experiences with evolutionary computation. The paper covers design and application mistakes as well as misperceptions in academia and industry. You can make a lot of technical mistakes in evolutionary computation. However, technical errors can be detected and rectified. Algorithms are implemented, presented and analysed by humans who also discuss and measure the impact of algorithms from their very individual perspectives. A lot of 'bugs' are actually not of a technical nature, but are human flaws. This text tries also to touch on these 'soft' aspects of evolutionary computing.