Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A key characteristic of Mixed-Integer (MI) problems is the presence of both continuous and discrete problem variables. These variables can interact in various ways, resulting in challenging optimization problems. ,  study the design of an algorithm that combines the strengths of LTGA and iAMaLGaM: state-of-the-art model-building EAs designed for discrete and con- tinuous search spaces, respectively.  examine and discuss issues which emerge when trying to integrate those two algorithms into the MI setting. Our consider- ations lead to a design of a new algorithm for solving MI problems, which  motivate and compare with alternative approaches. Mixed-Integer (MI) optimization problems arise in many real-world application do- mains. A key characteristic of MI problems is the presence of both continuous and discrete problem variables. Many studies exist on dealing with either continuous or discrete search spaces only.  are interested in studying if and how approaches origi- nally designed for real or discrete domains only can be integrated for the mixed-integer landscapes. More specifically,  consider two state-of-the-art model building EAs: LTGA (7) and iAMaLGaM (1). Both re previously shown to exhibit excellent polynomial scale- up behavior on various ll-known black-box benchmark problems.  wish to study if making use of the model building and learning abilities of both these algorithms can be applied to MI problems while retaining some of the excellent scale-up behavior. The model-building nature of these algorithms allows us to consider black-box prob- lems where no prior information about a problem structure is known. Some research on solving MI problems with EDAs has been discussed in (4), but was limited in terms of possible variable dependencies.  introduce an integrated implementation which relies on interleaving the model- building capabilities of both EAs. Integrative dependency processing is the holy grail of an approach using model-building algorithms to solve MI problems. It is important to first understand the capacity and limitations of an approach that interleaves existing individual models, as it allows us to better understand the requirements for integrative dependency processing. A crucial aspect of designing such algorithm is determining a way of maintaining a proper balance beten structure learning and offspring creation done by the two independent models. Obtaining such balance is a difficult task, as many
['Krzysztof L. Sadowski', 'Dirk Thierens', 'Peter A. N. Bosman']
I am writing a research paper on a idea Combining Model-Based EAs for Mixed-Integer Problems
[{"from": "human", "value": "I am writing a research paper on a idea Combining Model-Based EAs for Mixed-Integer Problems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A key characteristic of Mixed-Integer (MI) problems is the presence of both continuous and discrete problem variables. These variables can interact in various ways, resulting in challenging optimization problems. ,  study the design of an algorithm that combines the strengths of LTGA and iAMaLGaM: state-of-the-art model-building EAs designed for discrete and con- tinuous search spaces, respectively.  examine and discuss issues which emerge when trying to integrate those two algorithms into the MI setting. Our consider- ations lead to a design of a new algorithm for solving MI problems, which  motivate and compare with alternative approaches. Mixed-Integer (MI) optimization problems arise in many real-world application do- mains. A key characteristic of MI problems is the presence of both continuous and discrete problem variables. Many studies exist on dealing with either continuous or discrete search spaces only.  are interested in studying if and how approaches origi- nally designed for real or discrete domains only can be integrated for the mixed-integer landscapes. More specifically,  consider two state-of-the-art model building EAs: LTGA (7) and iAMaLGaM (1). Both re previously shown to exhibit excellent polynomial scale- up behavior on various ll-known black-box benchmark problems.  wish to study if making use of the model building and learning abilities of both these algorithms can be applied to MI problems while retaining some of the excellent scale-up behavior. The model-building nature of these algorithms allows us to consider black-box prob- lems where no prior information about a problem structure is known. Some research on solving MI problems with EDAs has been discussed in (4), but was limited in terms of possible variable dependencies.  introduce an integrated implementation which relies on interleaving the model- building capabilities of both EAs. Integrative dependency processing is the holy grail of an approach using model-building algorithms to solve MI problems. It is important to first understand the capacity and limitations of an approach that interleaves existing individual models, as it allows us to better understand the requirements for integrative dependency processing. A crucial aspect of designing such algorithm is determining a way of maintaining a proper balance beten structure learning and offspring creation done by the two independent models. Obtaining such balance is a difficult task, as many"}]