A hybrid genetic algorithm applied to automatic parallel controller code generation

Abstract

High performance real-time digital controllers employ parallel hardware such as transputers and digital signal processors t o achieve short response times when this is not achievable with conventional uni-processor systems. Implementing such fine-grained parallel software is dificult, error-prone and difficult. In this paper we show how a hybrid genetic algorithm can be applied to automate this parallel code generation for a set of regular control problems such that significant speedup is obtained with few constraints o n hardware. Genetic algorithms are particularly suited t o this problem since the mapping problem is combinatorial in nature. However, one drawback of the genetic algorithm is that it is sensitive t o small changes in the problem size. To overcome this problem the presented approach partitions the original problem into sub-problems, called boxes. The scheduling of these boxes is similar to the V L S I placem e n t problem.

A framework for evaluating continuous microservice delivery strategies

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Solving a real-time allocation problem with constraint programming

“…Since the problem of allocating tasks is generally NP-hard [34], some form of enumerative method or approximation using heuristics needs to be developed for this problem: graph theory techniques [57,38,18], branch-andbound [41,45,44,50,65], genetic algorithm [40,6,53,2,39,19,20,42,21], clustering [4,1,36], steepest descent (or Hill climbing) [40,64], tabu search [46,64]; simulated annealing [63,11,40,14,15,17,19,64], neural network [56,2], and dedicated heuristics [54,17,32,66,3]. However, today, for a specific problem, no technique seems to be more appropriate than another.…”

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