There are many potential reasons why work may be poorly received. Here are a few: insufficient explanation of methods and algorithms, insufficient experimental evidence, insufficient analysis, lack of statistical significance, lack of replicability, reading too much into one's results, insufficient novelty, poor presentation and poor English. In scientific, rather than commercial, work it is vital to report enough details so that someone else can reproduce your results. One very useful idea is to publish a table summarising your GP run. Table 4.1 (page 77 ) contains an example tableau.
As explained in Section 13.2 , it is essential to ensure that results are statistically significant so that nobody can dismiss them as the consequence of a lucky fluke. Complex ideas are often best explained by diagrams. When possible, descriptions of non-trivial algorithms should be accompanied by pseudocode, along with text describing the most important components of the algorithm.
In addition to reporting your results, make sure you also discuss their implications. If, for example, what GP has evolved means the customer can save money or could improve their process in some way, then this should be highlighted. Also be careful to not construct excessively complex explanations for the observations. It is very tempting to say "X is probably due to Y", but for this to be believable one should at least have made some attempt to check if Y is indeed taking place, and whether modulations or suppression of Y in fact produce modulations and/or suppression of X.
Finally, the most likely outcomes of a text that is badly written or badly presented are: 1) your readers will misunderstand you, and 2) you will have fewer readers. Spell checkers can help with typos, but whenever possible one should ensure a native English speaker has proofread the text.