Evolutionary psychology and the rehabilitation of offenders : constraints and consequences

The mainstream view in criminology and correctional psychology is that evolutionary perspectives have little to offer researchers or practitioners and may in fact lead to overly reductionist and crude responses to the complex issue of crime and its consequences. However, in our view all the major evolutionary approaches to the explanation of human behavior have something of value to contribute, with niche construction providing a particularly useful lens upon crime and its management. In this paper we analyze the concept of rehabilitation and describe the key features of effective rehabilitation. We argue that niche construction is responsive to the cognitive versatility and behavioral flexibility of human beings and can also accommodate the important role of social and cultural scaffolding in the rehabilitation process.

Evolutionary specialization of a tryptophan indole group for transition-state stabilization by eukaryotic transglutaminases

Covalent posttranslational protein modifications by eukaryotic transglutaminases proceed by a kinetic pathway of acylation and deacylation. Ammonia is released as the acylenzyme is formed, whereas the cross-linked product is released later in the deacylation step. Superposition of the active sites of transglutaminase type 2 (TG2) and the structurally related cysteine protease, papain, indicates that in the formation of tetrahedral intermediates, the backbone nitrogen of the catalytic Cys-277 and the N_Ɛ1 nitrogen of Trp-241 of TG2 could contribute to transition-state stabilization. The importance of this Trp-241 side chain was demonstrated by examining the kinetics of dansylcadaverine incorporation into a model peptide. Although substitution of the Trp-241 side chain with Ala or Gly had only a small effect on the Michaelis constant K_m (1.5-fold increase), it caused a >300-fold lowering of the catalytic rate constant k_cat. The wild-type and mutant TG2-catalyzed release of ammonia showed kinetics similar to the kinetics for the formation of cross-linked product, indicating that transitionstate stabilization in the acylation step was rate-limiting. In papain, a Gln residue is at the position of TG2-Trp-241. The conservation of Trp-241 in all eukaryotic transglutaminases and the finding that W241Q-TG2 had a much lower k_cat than wild-type enzyme suggest evolutionary specialization in the use of the indole group. This notion is further supported by the observation that transitionstate- stabilizing side chains of Tyr and His that operate in some serine and metalloproteases only partially substituted for Trp.

The effects of topological inaccuracy in evolutionary trees on the phylogenetic comparative method of independent contrasts

Computer simulations were used to test the effect of increasing phylogenetic topological inaccuracy on the results obtained from correlation tests of independent contrasts. Predictably, increasing the number of disruptions in the tree increases the likelihood of significant error in the r values produced and in the statistical conclusions drawn from the analysis. However, the position of the disruption in the tree is important: Disruptions closer to the tips of the tree have a greater effect than do disruptions that are close to the root of the tree. Independent contrasts derived from inaccurate topologies are more likely to lead to erroneous conclusions when there is a true significant relationship between the variables being tested (i.e., they tend to be conservative). The results also suggest that random phylogenies perform no better than nonphylogenetic analyses and, under certain conditions, may perform even worse than analyses using raw species data. Therefore, the use of random phylogenies is not beneficial in the absence of knowledge of the true phylogeny.

Conflict between genetic and phenotypic differentiation : the evolutionary history of a ‘lost and rediscovered’ shorebird

Understanding and resolving conflicts between phenotypic and genetic differentiation is central to evolutionary research. While phenotypically monomorphic species may exhibit deep genetic divergences, some morphologically distinct taxa lack notable genetic differentiation. Here we conduct a molecular investigation of an enigmatic shorebird with a convoluted taxonomic history, the White-faced Plover (Charadrius alexandrinus dealbatus), widely regarded as a subspecies of the Kentish Plover (C. alexandrinus). Described as distinct in 1863, its name was consistently misapplied in subsequent decades until taxonomic clarification ensued in 2008. Using a recently proposed test of species delimitation, we reconfirm the phenotypic distinctness of dealbatus. We then compare three mitochondrial and seven nuclear DNA markers among 278 samples of dealbatus and alexandrinus from across their breeding range and four other closely related plovers. We fail to find any population genetic differentiation between dealbatus and alexandrinus, whereas the other species are deeply diverged at the study loci. Kentish Plovers join a small but growing list of species for which low levels of genetic differentiation are accompanied by the presence of strong phenotypic divergence, suggesting that diagnostic phenotypic characters may be encoded by few genes that are difficult to detect. Alternatively, gene expression differences may be crucial in producing different phenotypes whereas neutral differentiation may be lagging behind.

Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

Induced ordered weighted averaging operators

Since the introduction of the ordered weighted averaging operator [18], the OWA has received great attention with applications in fields including decision making, recommender systems [8, 21], classification [10] and data mining [16] among others. The most important step in the calculation of the OWA is the permutation of the input vector according to the size of its arguments. In some applications, it makes sense that the inputs be reordered by values different to those used in calculation. For instance, if we have a number of mobile sensor readings, we may wish to allocate more importance to the reading taken from the sensor closest to us at a given point in time, rather than the largest reading.

Taxonomic review and evolutionary trends of Levipustulini and Absenticostini (Brachiopoda) from Argentina : palaeobiogeographic and palaeoclimatic implications

The diagnosis and composition of the brachiopod Tribe Levipustulini Lazarev, 1985 is reviewed, leading to a detailed revision of the genera Levipustula Maxwell, 1951 and Lanipustula Klets, 1983, as well as a review of previous records of the species Levipustula levis Maxwell from Australia and Argentina. The presence of Lanipustula patagoniensis Simanauskas in Patagonia is confirmed with additional topotypic material described and illustrated. Based on this review, we reassign Levipustula levis from New South Wales, Australia to Lanipustula. Two new species, Lanipustula kletsi from the middle Pennsylvanian of Patagonia and the Absenticostinin Absenticosta bruntoneileenae from the latest Viséan of western Argentina, are proposed. Abstenticosta bruntoneileenae is suggested as a possible ancestral stock of the Patagonian Levipustulini through the lineage Lanipustula-Verchojania-Jakutoproductus-Piatnitzkya (Serpukhovian-middle Artinskian). The development of similar phylogenetic lineages of Levipustulini in high latitude regions of both northern and southern hemispheres (such as Siberia in Northeast Asia and Patagonia in southwestern Gondwana) is here interpreted as a consequence of parallel evolution. The progressive palaeobiogeographic isolation of Patagonia from mainland South America, coupled with its southward drift under cold palaeoclimatic conditions during middle Carboniferous-earliest Permian times, is proposed to have triggered the Levipustulini vicariance.

A GA-PSO layered encoding evolutionary approach to 0/1 knapsack optimization

This paper presents a layered encoding cascade evolutionary approach to solve a 0/1 knapsack optimization problem. A layered encoding structure is proposed and developed based on the schema theorem and the concepts of cascade correlation and multi-population evolutionary algorithms. Genetic algorithm (GA) and particle swarm optimization (PSO) are combined with the proposed layered encoding structure to form a generic optimization model denoted as LGAPSO. In order to enhance the finding of both local and global optimum in the evolutionary search, the model adopts hill climbing evaluation criteria, feature of strength Pareto evolutionary approach (SPEA) as well as nondominated spread lengthen criteria. Four different sizes benchmark knapsack problems are studied using the proposed LGAPSO model. The performance of LGAPSO is compared to that of the ordinary multi-objective optimizers such as VEGA, NSGA, NPGA and SPEA. The proposed LGAPSO model is shown to be efficient in improving the search of knapsack’s optimum, capable of gaining better Pareto trade-off front.

An interactive evolutionary algorithm for MMIC low noise amplifier design

A layer-encoded interactive evolutionary algorithm (IEA) for optimization of design parameters of a monolithic microwave integrated circuit (MMIC) low noise amplifier is presented. The IEA comprises a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) technique. The layer-encoding structure allows human intervention in order to accelerate the process of evolution, whereas the GA and PSO technique are incorporated to enhance both global and local searches. With this combination of features, the proposed IEA has shown to be efficient in meeting all requirements and constraints of the MMIC. In addition, the IEA is able to optimize noise figure, current, and power gain of the MMIC amplifier design.

Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN) that combines the Fuzzy ARTMAP (FAM) network and a Hybrid Evolutionary Programming (HEP) model is introduced. The proposed FAM-HEP model, which combines the strengths of FAM and HEP, is able to construct its network structure autonomously as well as to perform learning and evolutionary search and adaptation concurrently. The effectiveness of the proposed FAM-HEP network is assessed empirically using several benchmark data sets and a real medical diagnosis problem. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP, FAM, and other classification models. In general, the results of FAM-HEP are better than those of FAM-EP and FAM, and are comparable with those from other classification models. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems in general, and a promising computerized decision support tool for tackling medical diagnosis tasks in particular.