This paper aimed at establishing a Corporate Entrepreneurship diagnosis model within public R&D institutions. We based our analysis on empirical identification of a generalized set of organizational factors, perceived as intrapreneurship vectors. The quantitative research targeted 50 experienced public entities and was based on validating one of the most popular psychometric instruments in the entrepreneurial literature: the Corporate Entrepreneurship Assessment Instrument (CEAI)—originally intended for the North American economic environment. As recent literature questioned the cross-cultural portability of psychometric instruments, this study intended to validate the five-factor intrinsic structure of CEAI. The five factors deduced by our statistical analysis were: support for opportunity investigations and reinforcement; dynamic environment and recognition; decreased formalization; knowledge sharing; time availability and strategic awareness. Next, the factor scores were used as input variables for a logistic regression procedure, with the output variable being the intrapreneurial value of the respondents’ institutions. Two factors contribute considerably to the predicted intrapreneurial value: support for opportunity investigations and reinforcement and decreased formalization. The validity of the whole approach is supported by the relevance of the original CEAI questionnaire, able to reveal intrapreneurial characteristics, and by the prediction power of the logistic regression model over the intrapreneurial propensity of public institutions.

Performance of evolutionary algorithms in real space is evaluated by local measures such as success probability and expected progress. In high-dimensional landscapes, most algorithms rely on the normal multi-variate, easy to assemble from independent, identically distributed components. This paper analyzes a different distribution, also spherical, yet with dependent components and compact support: uniform in the sphere. Under a simple setting of the parameters, two algorithms are compared on a quadratic fitness function. The success probability and the expected progress of the algorithm with uniform distribution are proved to dominate their normal mutation counterparts by order $n!!$.

Previous research proposed the uniform mutation inside the sphere as a new mutation operator for evolution strategies (continuous evolutionary algorithms), with a case study of the elitist algorithm on the SPHERE. For that landscape, one-step success probability and expected progress were estimated analytically, and further proved to converge, as space dimension increases, to the corresponding asymptotics of the algorithm with normal mutation. This article takes the analysis further by considering the RIDGE, an asymmetric landscape almost uncovered in the literature. For the elitist algorithm, estimates of expected progress along the radial and longitudinal axes are derived, then tested numerically against the real behavior of the algorithm on several functions from this class. The global behavior of the algorithm is predicted correctly by iterating the one-step analytical formulas. Moreover, experiments show identical mean value dynamics for the algorithms with uniform and normal mutation, which implies that the derived formulas apply also to the normal case. Essential to the whole analysis is $\theta $ , the inclination angle of the RIDGE. The behavior of the algorithm on the SPHERE and HYPERPLANE is also obtained, at the limits of the $\theta $ interval (0°, 90°].

The theory of evolutionary algorithms on continuous space gravitates around the evolution strategy with one individual, adaptive mutation and elitist selection, optimizing the symmetric, quadratic SPHERE function. The classic, normal mutation theory consists of three main building-blocks: i) a two-term formula for the local (constant mutation) expected progress; ii) an exponential formula for the global behavior of the (adaptive mutation) algorithm; iii) linear convergence time with respect to both space dimension n and (logarithm of) initial distance to optimum. We show that the three main results still hold if we replace the normal mutation with uniform inside the sphere and also with the sum of two uniforms. That makes the case for an important conclusion: the linear convergence time of the algorithm is not a consequence of the normal mutation, but of the elitist selection and 1/5 success rule. A simplified version of the 1/5-rule allows also for an intuitive representation of the algorithm, as a sequence of constant-mutation, independent and identical (expected) length cycles.

For large space dimensions, the log-linear convergence of the elitist evolution strategy with a 1/5 success rule on the sphere fitness function has been observed, experimentally, from the very beginning. Finding a mathematical proof took considerably more time. This paper presents a review and comparison of the most consistent theories developed so far, in the critical interpretation of the author, concerning both global convergence and the estimation of convergence rates. I discuss the local theory of the one-step expected progress and success probability for the (1+1) ES with a normal/uniform distribution inside the sphere mutation, thereby minimizing the SPHERE function, but also the adjacent global convergence and convergence rate theory, essentially based on the 1/5 rule. Small digressions into complementary theories (martingale, irreducible Markov chain, drift analysis) and different types of algorithms (population based, recombination, covariance matrix adaptation and self-adaptive ES) complete the review.