In order to propose an evolutionary path for a large business' telecommunications service, one needs a synoptic view of that customer's existing services suitable for systemic analysis. Only with such a view can a telecommunications provider enter into a truly consultative relation with large customers. The Network Map application provides such a view by displaying service information contained in a conventional relational database through a Hyperbolic Tree viewer.

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Portfolio managers are typically constrained by turnover limits, minimum and maximum stock positions, cardinality, a target market capitalization and sometimes the need to hew to a style (such as growth or value). In addition, portfolio managers often use multifactor stock models to choose stocks based upon their respective fundamental data.We use multiobjective evolutionary algorithms (MOEAs) to satisfy the above real-world constraints. The portfolios generated consistently outperform typical performance benchmarks and have statistically significant asset selection.In finance, a portfolio is a collection of assets held by an institution or a private individual. The portfolio selection problem seeks the optimal way to distribute a given monetary budget on a set of available assets. The problem usually has two criteria: expected return to be maximized and risk to be minimized. Classical mean-variance portfolio selection aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio risk. In the case of linear equality and inequality constraints, the problem can be solved efficiently by quadratic programming, i.e., variants of Markowitz"s critical line algorithm. What complicates this simple 1 Andrew Clark, Chief Index Strategist, Thomson Reuters Indices & Lipper, andrew.clark@thomsonreuters.com 2 Jeff Kenyon, Lead Software Engineer, Thomson Reuters Indices, jeff.kenyon@thomsonreuters.com statement of portfolio construction are the typical real-world constraints that are by definition non-convex, e.g. cardinality constraints which limits the number of assets in a portfolio and minimum and maximum buy-in thresholds. In what follows, we use multi-objective evolutionary algorithms (MOEAs) 3 as an active set algorithm optimized for portfolio selection.The MOEAs generate the set of all feasible portfolios (those portfolios meeting the constraints), calculates the efficient frontier for each and also their respective Sharpe ratio. The portfolio with the best Sharpe ratio becomes the portfolio used for the next time period. We chose MOEAs to solve a non convex optimization problem because there are certain outstanding problems in terms of their use: 1) In the literature MOEAs have not been used to solve multiperiod financial problems (or multi-period problems in general), 2) The number and types of constraints in a real world financial portfolio problem exceeds what has been done with MOEAs so far and 3) It is not known if MOEA stock selection is statistically significant. We answer all of these questions with a yes thereby advancing the understanding and use of MOEAs. This especially true when it comes to solving a moderately difficult, multi-period real world problem such as those encountered in finance.