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The study presented in this paper is a controlled experiment, aiming at investigating the impact of a design heuristic, dealing with the 'god class' problem, on the maintainability of object-oriented designs.
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Therefore, CMLPSA is in principle similar to meta-heuristic algorithms dealing with a pool/population of designs rather than with a single trial point such as it is usually done in classical simulated annealing.
This has led to a vast literature on heuristics for dealing with this problem as accurately as possible.
The BWA, Bowtie and SOAP2 aligners implement a number of strategies and heuristics for dealing with base mismatches and small insertion/deletions (Langmead et al., 2009; Li and Durbin, 2009; Li et al., 2009).
(Below we describe a heuristic to deal with highly repetitive seeds).
(1) The classical heuristics-and-biases program [15] assumes that people use heuristics to deal with the limitations of the cognitive system, leading to systematic errors and lapses of reasoning that point to human irrationality.
Genetic Algorithm (GA) is a popular heuristic method for dealing complex problems with very large search space.
With flexible representations, GP can represent and evolve effective scheduling heuristics to deal with a wide range of scheduling problems.
In Yin et al. [142], a GP technique is proposed to evolve predictive scheduling heuristics to deal with stochastic machine breakdowns.
Basically, these heuristics can deal easily with most dynamic changes such as dynamic job arrivals, machine breakdowns, and stochastic processing times.
The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments.
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