@article{Si:2006:BMC,
        title = {{QSAR} study of 1,4-dihydropyridine calcium channel
                 antagonists based on gene expression programming},
        author = {Hong Zong Si and Tao Wang and Ke Jun Zhang and Zhi De Hu and Bo Tao Fan},
        journal = {Bioorganic \& Medicinal Chemistry},
        month = {15 July},
        number = 14,
        pages = {4834--4841},
        volume = 14,
        year = 2006,
        doi = {doi:10.1016/j.bmc.2006.03.019},
	abstract = {The gene expression programming, a novel machine
                 learning algorithm, is used to develop quantitative
                 model as a potential screening mechanism for a series
                 of 1,4-dihydropyridine calcium channel antagonists for
                 the first time. The heuristic method was used to search
                 the descriptor space and select the descriptors
                 responsible for activity. A nonlinear, six-descriptor
                 model based on gene expression programming with
                 mean-square errors 0.19 was set up with a predicted
                 correlation coefficient (R2) 0.92. This paper provides
                 a new and effective method for drug design and
                 screening.

                 Graphical abstract

                 The log (1/IC50) for 45 1,4-dihydropyridines was
                 modelled using the descriptors calculated from the
                 molecular structure along with a quantitative
                 structure\u2013activity relationship (QSAR) technique.
                 The heuristic method (HM) and gene expression
                 programming (GEP) were used to construct the linear and
                 nonlinear prediction models, leading to a good
                 prediction.},
	biburl = {http://www.bibsonomy.org/bibtex/268bfa0bc6e1d96ad3c70488119d68fc1/brazovayeye},
	keywords = {Programming, QSAR, Gene Calcium programming, genetic Expression antagonists algorithms, channel}
}

@article{Salcedo-Sanz:2005:GPEM,
        title = {Meta-Heuristic Algorithms for {FPGA} Segmented Channel
                 Routing Problems with Non-standard Cost Functions},
        author = {Sancho Salcedo-Sanz and Yong Xu and Xin Yao},
        journal = {Genetic Programming and Evolvable Machines},
        month = {December},
        number = 4,
        pages = {359--379},
        volume = 6,
        year = 2005,
        issn = {1389-2576},
	doi = {doi:10.1007/s10710-005-3295-z},
	size = {21 pages},
	abstract = {we present three meta-heuristic approaches for FPGA
                 segmented channel routing problems (FSCRPs) with a new
                 cost function in which the cost of each assignment is
                 not known in advance, and the cost of a solution only
                 can be obtained from entire feasible assignments.
                 Previous approaches to FSCPs cannot be applied to this
                 kind of cost functions, and meta-heuristics are a good
                 option to tackle the problem. We present two hybrid
                 algorithms which use a Hopfield neural network to solve
                 the problem's constraints, mixed with a Genetic
                 Algorithm (GA) and a Simulated Annealing (SA). The
                 third approach is a GA which manages the problem's
                 constraints with a penalty function. We provide a
                 complete analysis of the three metaheuristics, by
                 tested them in several FSCRP instances, and comparing
                 their performance and suitability to solve the FSCRP.},
	biburl = {http://www.bibsonomy.org/bibtex/2f2d0f72766685f051d19c41f65ab2c4f/brazovayeye},
	keywords = {simulated architecture, hardware, FPGAs, genetic segmented hybrid evolvable algorithms, channel annealing}
}