Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of ...Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.展开更多
Quantitative correlation of several theoretical electrophilicity measures over different families of organic compounds are examined relative to the experimental values of Mayr et al.Notably,the ability to predict thes...Quantitative correlation of several theoretical electrophilicity measures over different families of organic compounds are examined relative to the experimental values of Mayr et al.Notably,the ability to predict these values accurately will help to elucidate the reactivity and selectivity trends observed in charge-transfer reactions.A crucial advantage of this theoretical approach is that itprovides this information without the need of experiments,which are often demanding and time-consuming.Here,two different types of electrophilicity measures were analyzed.First,models derived from conceptual density functional theory(c-DFT),including Parr's original proposal and further generalizations of this index,are investigated.For instance,the approaches of Gázquez et al.and Chamorro et al.are considered,whereby it is possible to distinguish between processes in which a molecule gains or loses electrons.Further,we also explored two novel electrophilicity definitions.On one hand,the potential of environmental perturbations to affect electron incorporation into a system is analyzed in terms of recent developments in c-DFT.These studies highlight the importance of considering the molecular surroundings when a consistent description of chemical reactivity is needed.On the other hand,we test a new definition of electrophilicity that is free from inconsistencies(so-called thermodynamic electrophilicity).This approach is based on Parr's pioneering insights,though it corrects issues present in the standard working expression for the calculation of electrophilicity.Additionally,we use machine-learning tools(i.e.,symbolic regression)to identify the models that best fit the experimental values.In this way,the best possible description of the electrophilicity values in terms of different electronic structure quantities is obtained.Overall,this straightforward approach enables one to obtain good correlations between the theoretical and experimental quantities by using the simple,yet powerful,interpretative advantage of c-DFT methods.In general,we observed that the correlations found at the HF/6-31 G(d)level of theory are of semi-quantitative value.To obtain more accurate results,we showed that working with families of compounds with similar functional groups is indispensable.展开更多
文摘Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.
基金support by FONDECYT(1140313)Financiamiento Basal para Centros Cientificos y Tecnoldgicos de Excelencia-FB0807project RC-130006 CILLIS,Chile
文摘Quantitative correlation of several theoretical electrophilicity measures over different families of organic compounds are examined relative to the experimental values of Mayr et al.Notably,the ability to predict these values accurately will help to elucidate the reactivity and selectivity trends observed in charge-transfer reactions.A crucial advantage of this theoretical approach is that itprovides this information without the need of experiments,which are often demanding and time-consuming.Here,two different types of electrophilicity measures were analyzed.First,models derived from conceptual density functional theory(c-DFT),including Parr's original proposal and further generalizations of this index,are investigated.For instance,the approaches of Gázquez et al.and Chamorro et al.are considered,whereby it is possible to distinguish between processes in which a molecule gains or loses electrons.Further,we also explored two novel electrophilicity definitions.On one hand,the potential of environmental perturbations to affect electron incorporation into a system is analyzed in terms of recent developments in c-DFT.These studies highlight the importance of considering the molecular surroundings when a consistent description of chemical reactivity is needed.On the other hand,we test a new definition of electrophilicity that is free from inconsistencies(so-called thermodynamic electrophilicity).This approach is based on Parr's pioneering insights,though it corrects issues present in the standard working expression for the calculation of electrophilicity.Additionally,we use machine-learning tools(i.e.,symbolic regression)to identify the models that best fit the experimental values.In this way,the best possible description of the electrophilicity values in terms of different electronic structure quantities is obtained.Overall,this straightforward approach enables one to obtain good correlations between the theoretical and experimental quantities by using the simple,yet powerful,interpretative advantage of c-DFT methods.In general,we observed that the correlations found at the HF/6-31 G(d)level of theory are of semi-quantitative value.To obtain more accurate results,we showed that working with families of compounds with similar functional groups is indispensable.
