Abstract—In this paper, we present a Portfolio optimization method based on Interactive Genetic Algorithm and a Fuzzy satisfaction function. Portfolio optimization is a formal mathematical approach to making investment decisions across a collection of financial instruments or assets. We will be using the classical approach, known as modern portfolio theory (MPT), that involves categorizing the investment universe based on risk (standard deviation) and return, and then choosing the mix of investments that achieve a desired risk versus return tradeoff. Genetic algorithms are stochastic search algorithms inspired by biological phenomena of genetic recombination and natural selection. They simulate the evolution of string individuals encoding candidate solutions to a given problem. Genetic algorithms proved robust and efficient in finding near-optimal solutions in complex problem spaces. They are usually exploited as an optimization method, suitable for both continuous and discrete optimization tasks. We present in our proposed method an Interactive Genetic Algorithm, since it is difficult to introduce a fitness function for this kind of problem, and we will exploit instead the user/expert knowledge by interacting with our method. Finally, we will discuss and evaluate the proposed solutions by using a Fuzzy satisfaction function that takes into account the investor’s subjective preference toward risk and/or return. 
 

Index Terms—interactive genetic algorithm, portfolio optimization, decision making, fuzzy satisfaction

Cite: Masato Sasaki, Anas Laamrani, Mitsuo Yamashiro, Chalew Alehegn, and Ariel Kamoyedji, "Portfolio Optimization by Fuzzy Interactive Genetic Algorithm" Journal of Advanced Management Science, Vol. 6, No. 3, pp. 124-131, September 2018. doi: 10.18178/joams.6.3.124-131