Abstract: A method and system are presented for configuring a search algorithm for solving a combinatorial optimization problem. The search algorithm has a number of procedural components. Each procedural component is configured using a respective data structure. The data structure has a tree structure, including traversal split nodes, each of which represents a choice to be made when configuring the procedural component of the search algorithm. Configuring the procedural component corresponds to traversing the tree structure, and the traversal is performed automatically. At each of the traversal split nodes, the choice of which direction to take through the tree structure is made randomly, using a respective probabilistic function. Thus, a population of candidate algorithms is generated. These are evaluated, and the probabilistic functions modified.

Abstract: The invention pertains to an algorithm development environment for solving a class of combinatorial optimization problems. Many practical real-life applications can be formulated as combinatorial optimization problems. Over the years, there have been algorithms proposed to solve these problems. The effort in customizing algorithms to fulfill a particular domain-specific application is still significant. Furthermore, conventional approaches towards codes generation and modification are tedious and thus inefficient. To address the need for rapid generation of algorithms that are efficient in solving a given class of real-life problems, embodiments provide a hierarchical tree structure for managing a procedure modules library. Based on the preferred management and object-oriented design concept, users configure and generate a genetic algorithm (GA) via an intuitive graphical user interface. The goal-seeking customization of the generated GA can be easily carried out for solving various optimization problems.

Abstract: A methodology is presented to address the need for rapid generation and optimization of algorithms that are efficient in solving a given class of problems within the framework of a software environment. The environment incorporates an evolutionary learning methodology which automatically optimizes the configurations of procedural components of the algorithm. In this way, both the efficiency and the quality of algorithm development is enhanced significantly.

Abstract: The invention pertains to an algorithm development environment for solving a class of combinatorial optimization problems. Many practical real-life applications can be formulated as combinatorial optimization problems. Over the years, there have been many well-known algorithms proposed to solve these problems. The effort in customizing algorithms to fulfill a particular domain-specific application is still significant. Furthermore, conventional approaches towards codes generation and modification are tedious and thus inefficient. To address the need for rapid generation of algorithms that are efficient in solving a given class of real-life problems, embodiments of the present invention encompasses a hierarchical tree structure for managing a procedure modules library. Based on the preferred management and object-oriented design concept, users configure and generate a genetic algorithm (GA) via an intuitive graphical user interface.