Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. Hover, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular sub-set.  also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off beten the performance and generality of the heuristics generated and their applicability to new problems.
['Edmund K. Burke', 'Matthew R. Hyde', 'Graham Kendall', 'John R. Woodward']
I am writing a research paper on a idea Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one
[{"from": "human", "value": "I am writing a research paper on a idea Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. Hover, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular sub-set. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are human-competitive over a range of sets of problems, or which excel on a particular sub-set.  also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the trade-off beten the performance and generality of the heuristics generated and their applicability to new problems."}]