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Heuristic (computer science) In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover") is a technique designed for problem solving more quickly when classic methods are too slow for finding an exact or approximate solution, or when classic methods fail to find any exact solution in a search space.
Agile software development is an umbrella term for approaches to developing software that reflect the values and principles agreed upon by The Agile Alliance, a group of 17 software practitioners in 2001. [1] As documented in their Manifesto for Agile Software Development the practitioners value: [2] Individuals and interactions over processes ...
Optimization problems are often multi-modal; that is, they possess multiple good solutions. They could all be globally good (same cost function value) or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer.
In software engineering, a design pattern describes a relatively small, well-defined aspect (i.e. functionality) of a computer program in terms of how to write the code. Using a pattern is intended to leverage an existing concept rather than re-inventing it. This can decrease the time to develop software and increase the quality of the ...
A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.
As a process of designing. An iterative, non-linear process, design thinking includes activities such as context analysis, user testing, problem finding and framing, ideation and solution generating, creative thinking, sketching and drawing, prototyping, and evaluating. Core features of design thinking include the abilities to: employ non ...
A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. [1] In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time.
According to Forrester Research, solution architecture is one of the key components by which Enterprise Architecture delivers value to the organization. It entails artifacts such as a solution business context, a solution vision and requirements, solution options (e.g. through RFIs, RFPs or prototype development) and an agreed optimal solution with build and implementation plans ("road-map").
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