Optimization of portfolios with and without constraints using evolutionary algorithms - Siddhant Gupta. (2009)
Diversification through portfolio construction has become an increasingly important tool in finance for minimizing risk associated with investment. There are two objectives that need to be optimized during portfolio constructions, thus making it a real-world multi-objective optimization problem. One quantitative approach that has generated considerable interest and extensive practice in the past few years is asset allocation. Very simply defined, asset allocation is the process of selecting a mix of asset classes and allocating capital to those assets by matching rates of return to a specified and quantifiable tolerance for risk. The idea of applying the biological principle of natural evolution to artificial systems, introduced more than three decades ago, has seen impressive growth in the past few years. Usually grouped under the term evolutionary algorithms or evolutionary computation, we find the domains of genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. Evolutionary algorithms are ubiquitous nowadays, having been successfully applied to numerous problems from different domains, including optimization, automatic programming, machine learning, economics, operations research, ecology, population genetics, studies of evolution and learning, and social systems.