Creating a GA algorithm for intraday trading (e.g., futures ES, NQ) is more difficult than textbook examples for GA function minimization/maximization. Initially, I assumed the parameters for buys and sells could be placed within each chromosome, but am now thinking that each chromosome can only represent a buy or a sell, where a single buy/sell parameter (gene) $x$ on the chromosome would result in a buy if $x>0.5$ and sell if $x \leq 0.5$ given parameter range [0,1].
Another challenge I am facing is that many of my rules are discrete (true/false) rather than continuously-scaled levels of indicators such as RSI level, ADX level, etc. For example, I have dozens of binary (0-no,1-yes) cross-over and cross-below type rules which are true/false. For these rules (genes), I am assuming boolean logic like buy=true if $x>0.5$.
The last challenge is that with many cross-over and cross-below rules, the odds that most of them are true for a given bar is unlikely, so the problem becomes one of finding the best combination of rules that e.g. maximizes the Sharpe(Sortino) ratio. In light of the above, would it be necessary to perhaps initialize each chromosome such that only one gene is set to $x=0.75$ and all other rules set to $x=0.25$, so that initially the fitness value (Sharpe) will be based on buys/sells if a single rule is true?
Certainly, there has to be a "trick" when using GAs when a lot of rules are considered for each chromosome, and the chances of e.g. 10 rules being true is rare, since only a few bars per 10,000 bars would have 10 rules being true.
One thing is certain with any GA: the value of every gene on a chromosome must be reflected in fitness, in other words, you can't have genes that trigger actions that don't affect fitness. So if fitness is like the height of a person (objective function), you can't have genes that encode eye or hair color, since those won't help minimize/maximize height.
Are there any classic papers (chapters) which describe chromosome setup for lots of binary (yes/no) trading rules?