Genetic Algorithms: Playing God: Elitism vs Egalitarianism

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Genetic Algorithms (GA) are a class of Artificial Intelligence that encode your model variables as chromosomes. And then you breed them over hundreds of generations mimicking evolution, using crossover and mutation of genes to improve the species.

GA applications include optimizing airline schedules, discovery of new drugs and new metal alloys, optimizing capacity of transportation systems etc. But I don’t have the data to practice on these topics, so I use GA to optimize portfolio strategies for the 30 constituents of the Straits Times Index (STI) and compare their performance. The two GA approaches are (1) Elitism where parents compete in a tournament (like the Hunger Games) and  only high-performance parents are allowed to breed and in each generation the children with good performance become parents, and the weak performance offspring are killed. (2) Egalitarianism where any compatible parents are allowed to marry and produce offspring.  The performance of strategies 1 and 2 are compared together with the performance of the STI, and also a strategy of just buying the three local banks (DBS, UOB and OCBC).

In the chromosome diagram Parent A and Parent B mate and after crossover and mutation of genes the composition of the genes has changed a lot.  The colors represent the stocks (genes) in the chromosomes. For example, Seatrium and Singtel are no longer present.  And the final chromosome contains only Wilmar, Keppel, SIA, DBS and UOB. 

You can see from the chart which shows what $1.00 invested on 2 Jan 2024 would be worth as at 2 July 2025 if the portfolio is held. Just buying the 3 banks (Green) perform best. The other two strategies (Blue and Red) underperformed the STI (Purple) but Egalitarianism performs better than Elitism. The Table below summarizes the performance of the strategies.  You can see that 3-Banks portfolio has highest total returns %, compounded annual growth rate, and Sharpe Ratio, but also a slightly higher volatility. 





 

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