Artificial Intelligence: Genetic Algorithms are forbidden to commit Incest

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Genetic Algorithms (GA) are a type of AI that mimics the evolutionary process of survival of the fittest. With GA you can solve multi-objective multi-constraint problems such as designing a resilient financial portfolio of securities, allocating a fleet of buses, or planes to routes using the least resources, or selecting potential combination of compounds or alloys that lead to discovery of new drugs or materials.

In a past post on my Finance blog I wanted to design an ultra-low risk portfolio consisting of a portfolio of ETFs from 6 Asia Pacific countries that would mitigate the effects of the MAGA President's unpredictable actions. The 6 countries were: Singapore, Hong Kong, India, Malaysia, Thailand and Indonesia. Their country stock Exchange Indices are given below.

Genetic Algotithms seemed to be really the best tool if you equate gene pool resilience with diversity just like in Nature as generations evolve through crossover and mutation of genes. In this specific case GAs are created by representing the population to be optimized as Chromosomes. The position size (number of shares holding) of each country's ETF would be the parameter e.g. aHSI, bSTI etc As the GA is run, thousands of combinations of parameters of the 6 variables are pitted against each other to evolve the best portfolio that fits our objective.

The objective is to maximize variance through breeding over hundreds of generations with crossover and mutation of genes. Fitness Function is defined against this objective and the fitter candidates have a higher probability of mating to be parents of the next generation.

However, the program could not converge because the input variables had too much common DNA, amounting to committing digital Incest if they mate, which means that the program cannot terminate  and you cannot achieve your objective for a more resilient and diverse population no matter how many generations of crossover and mutation of genes are carried out.  
In the above Table we show you how much common DNA there is between Hang Seng (HSI), Sensex, STI, JKSE, KLSE and SETI. We use a metric known as Mutual Information (MI) which, unlike Correlation, can handle non-linear inter-relationships. MI is defined as that which reduces the amount of Uncertainty between variables. The Table above shows that except for JKSE and SETI, all other markets have very high MI >1 measured against the DJIA. Which means that its easier to use the DJIA to construct prediction models of STI and HSO markets. But which also means that you cannot create the necessary diversity in your portfolio for resilience. 

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