Introduction
Recently, we have witnessed an intensive development of stochastic non-linear optimization methods inspired by some biological, thermodynamic or genetic ideas. Typical methods within this class are Evolution Strategies [1, 5], Simulated Annealing [2, 3] and Genetic Algorithms [3, 4]. These methods, though being usually treated as completely independent, also share many similarities [1-5, 14, 15, 17]. Some critical features of these methods may be substantially improved by introducing a concept of Meta-optimization [15]. Following our previous MENDEL's paper [18], a rationale underlying this generalized view on the stochastic optimizations may be outlined as follows:

During a long history of biological evolution, nature "tested" various optimization strategies. Systems ("species") performing some ineffective optimization strategies vanished because of their limited adaptibility to their environment. Thus, if a biological system has been created as a result of a long evolutionary process, this process would be inevitably an optimized optimization strategy in its final stage. The evolution is a meta-optimizing strategy, which adapts not only a system to be optimized, but also the optimization process itself.

Meta-optimizing algorithms should optimize not only some degrees of freedom, but also some auxiliary parameters governing the optimization itself. Having natural examples of evolutionary optimizations available, one can learn not only some optimizing mechanisms, but also their meta-optimization settings (mortality, variability, etc.). It was discovered that the best evolutionary adaptability is reached only within some narrow "evolutionary window". Consequently, a surprising interpretation of some biological facts (e.g., mortality and variability of species) is possible via purely mathematical features of some Meta-Evolutionary algorithms. Note also that conventional Evolution Strategies or Genetic Algorithms mimic actually an old Lamarck's theory of heredity (a direct transfer of "optimizing experience" via genotypes). A modern view by Darwin and Mendel is better simulated by the concept of Meta-Evolutionary optimization. These ideas was discovered independently by several authors (Rechenberg [6], Gottvald [18, 15]). For more discussion and particular Meta-Evolutionary algorithms, the reader is referred to our previous papers [17-19]. Some archetypes of meta-optimizing (meta-genetic) algorithms may also be traced in [23, 24, 25].

Meta-Evolutionary algorithms generate a class of optimization trajectories, which implies some useful features [15, 17, 19]: (i) enforced global convergence; (ii) lower sensitivity to initial configurations; (iii) lower sensitivity to auxiliary optimization parameters; (iv) detailed uncertainty analysis; (v) unified implementation of several global optimization techniques (multistart, relaxation, tabu-search, etc.) [9, 17-19].

In the present paper, a Meta-Evolutionary optimization will be applied to quantifying signal parameters in biomedical Magnetic Resonance Spectroscopy (MRS) [10, 11, 17, 20]. By contrast to conventional Fourier-based quantifications, this Evolutionary MRS is less susceptible to many artifacts. Consequently, it shows much promise in biomedical MRS under various extreme conditions (inhomogeneity, noise, truncations, etc.).

References
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GIOM
Selected publications