Adaptability in organisms and artifacts: A multi level perspective on adaptive processes

Abstract Adaptability to changing circumstances is a key feature of living creatures. Understanding such adaptive processes is central to developing successful autonomous artifacts. In this paper two perspectives are brought to bear on the issue of adaptability. The first is a short term perspective which looks at adaptability in terms of the interactions between the agent and the environment. The second perspective involves a hierarchical evolutionary model which seeks to identify higher-order forms of adaptability based on the concept of adaptive meta-constructs. Task orientated and agent-centered models of adaptive processes in artifacts are considered from these two perspectives. The former isrepresented by the fitness function approach found in evolutionary learning, and the latter in terms of the concepts of empowerment and homeokinesis found in models derived from the self-organizing systems approach. A meta-construct approach to adaptability based on the identification of higher level meta-metrics is also outlined. 2009 Published by Elsevier B.V.

Linear QR architecture for a single chip adaptive beamformer

This paper presents the design of a novel single chip adaptive beamformer capable of performing 50 Gflops, (Giga-floating-point operations/second). The core processor is a QR array implemented on a fully efficient linear systolic architecture, derived using a mapping that allows individual processors for boundary and internal cell operations. In addition, the paper highlights a number of rapid design techniques that have been used to realise this system. These include an architecture synthesis tool for quickly developing the circuit architecture and the utilisation of a library of parameterisable silicon intellectual property (IP) cores, to rapidly develop detailed silicon designs.

Phenotype-environment correlations in a putative whitefish adaptive radiation

1. The adaptive radiation of fishes into benthic (littoral) and pelagic (lentic) morphs in post-glaciallakes has become an important model system for speciation. Although these systems are well stud-ied, there is little evidence of the existence of morphs that have diverged to utilize resources in theremaining principal lake habitat, the profundal zone.
2. Here, we tested phenotype-environment correlations of three whitefish (Coregonus lavaretus)morphs that have radiated into littoral, pelagic and profundal niches in northern Scandinavianlakes. We hypothesized that morphs in such trimorphic systems would have a morphology adaptedto one of the principal lake habitats (littoral, pelagic or profundal niches). Most whitefish popula-tions in the study area are formed by a single (monomorphic) whitefish morph, and we furtherhypothesized that these populations should display intermediate morphotypes and niche utiliza-tion. We used a combination of traditional (stomach content, habitat use, gill raker counts) andmore recently developed (stable isotopes, geometric morphometrics) techniques to evaluate pheno-type-environment correlations in two lakes with trimorphic and two lakes with monomorphicwhitefish.
3. Distinct phenotype-environment correlations were evident for each principal niche in whitefishmorphs inhabiting trimorphic lakes. Monomorphic whitefish exploited multiple habitats, hadintermediate morphology, displayed increased variance in gillraker-counts, and relied significantlyon zooplankton, most likely due to relaxed resource competition.
4. We suggest that the ecological processes acting in the trimorphic lakes are similar to each other,and are driving the adaptive evolution of whitefish morphs, possibly leading to the formation ofnew species.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.